223 10 20655 http://socictopen.socict.org/files/original/c2f97135cc17c88b02037724a06c6b12.pdf 2c55fee6e5e4c994bbae3d6827d92701 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System Creator An entity primarily responsible for making the resource Esposito A, Casiraghi E, Chiaraviglio F, Scarabelli A, Stellato E, Plensich G, Lastella G, Di Meglio L, Fusco S, Avola E, Jachetti A, Giannitto C, Malchiodi D, Frasca M, Beheshti A, Robinson PN, Valentini G, Forzenigo L, Carrafiello G Description An account of the resource Andrea Esposito,1 Elena Casiraghi,2 Francesca Chiaraviglio,1 Alice Scarabelli,3 Elvira Stellato,3 Guido Plensich,3 Giulia Lastella,1 Letizia Di Meglio,3 Stefano Fusco,3 Emanuele Avola,3 Alessandro Jachetti,4 Caterina Giannitto,5 Dario Malchiodi,2 Marco Frasca,2 Afshin Beheshti,6,7 Peter N Robinson,8,9 Giorgio Valentini,2 Laura Forzenigo,1 Gianpaolo Carrafiello1 1Radiology Department, Foundation IRCCS Ospedale Maggiore Policlinico Hospital, Milan, 20122, Italy; 2Anacleto Lab, Computer Science Department, University of Milan, Milan, 20133, Italy; 3Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, 20122, Italy; 4Accident and Emergency Department, Foundation IRCCS Ospedale Maggiore Policlinico Hospital, Milan, 20122, Italy; 5Radiology Department, Humanitas Research Hospital, Milan, 20013, Italy; 6KBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, 94035, USA; 7Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA; 8The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA; 9Institute for Systems Genomics, University of Connecticut, Farmington, CT, 06030, USACorrespondence: Elvira Stellato; Alice Scarabelli Email elvira.stellato@gmail.com; alice.scarabelli1105@gmail.comPurpose: To determine the performance of a chest radiograph (CXR) severity scoring system combined with clinical and laboratory data in predicting the outcome of COVID-19 patients.Materials and Methods: We retrospectively enrolled 301 patients who had reverse transcriptase-polymerase chain reaction (RT-PCR) positive results for COVID-19. CXRs, clinical and laboratory data were collected. A CXR severity scoring system based on a qualitative evaluation by two expert thoracic radiologists was defined. Based on the clinical outcome, the patients were divided into two classes: moderate/mild (patients who did not die or were not intubated) and severe (patients who were intubated and/or died). ROC curve analysis was applied to identify the cut-off point maximizing the Youden index in the prediction of the outcome. Clinical and laboratory data were analyzed through Boruta and Random Forest classifiers.Results: The agreement between the two radiologist scores was substantial (kappa = 0.76). A radiological score &ge; 9 predicted a severe class: sensitivity = 0.67, specificity = 0.58, accuracy = 0.61, PPV = 0.40, NPV = 0.81, F1 score = 0.50, AUC = 0.65. Such performance was improved to sensitivity = 0.80, specificity = 0.86, accuracy = 0.84, PPV = 0.73, NPV = 0.90, F1 score = 0.76, AUC= 0.82, combining two clinical variables (oxygen saturation [SpO2]), the ratio of arterial oxygen partial pressure to fractional inspired oxygen [P/F ratio] and three laboratory test results (C-reactive protein, lymphocytes [%], hemoglobin).Conclusion: Our CXR severity score assigned by the two radiologists, who read the CXRs combined with some specific clinical data and laboratory results, has the potential role in predicting the outcome of COVID-19 patients.Keywords: radiography, thoracic, COVID-19, artificial intelligence, prognosis Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource covid-19, prognosis, artificial intelligence, thoracic, Radiography Source A related resource from which the described resource is derived Biotemas Publisher An entity responsible for making the resource available Universidade Federal de Santa Catarina Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medical physics. Medical radiology. Nuclear medicine http://socictopen.socict.org/files/original/36fe7cc1d9df535a1fd7d0e0863089b1.pdf 073e8a4cc51b4d6fb7972693f205b20f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Intelligence in the Agri-Food System: Rethinking Sustainable Business Models in the COVID-19 Scenario Creator An entity primarily responsible for making the resource Assunta Di Vaio, Loris Landriani, Flavio Boccia, Rosa Palladino Description An account of the resource The aim of the paper is to investigate the artificial intelligence (AI) function in agri-food industry, as well as the role of stakeholders in its supply chain. Above all, from the beginning of the new millennium, scholars and practitioners have paid an increasing attention to artificial intelligence (AI) technologies in operational processes management and challenges for new business models, in a sustainable and socially responsible perspective. Thus, the stakeholders can assume a proactive or marginal role in the value creation for business, according to their own environmental awareness. These issues appear still “open” in some industries, such as the agri-food system, where the adoption of new technologies requires rethinking and redesigning the whole business model. Methodologically, we brought forward an in-depth review of the literature about major articles in this field. Especially, the study has been conducted following two phases: firstly, we extracted from scientific databases (Web of Science, Scopus, and Google Scholar) and studied relevant articles; secondly, we analyzed the selected articles. The findings highlight interesting issues about AI towards a “space economy” to achieve sustainable and responsible business models, also in the perspective of the COVID-19 pandemic scenario. Theoretical and managerial implications are discussed. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Environmental awareness, Literature review, environmental space, stakeholder, space economy Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/su12124851 Source A related resource from which the described resource is derived Sustainability Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Environmental sciences, Renewable energy sources, Environmental effects of industries and plants http://socictopen.socict.org/files/original/9e88ed5c267be3292bc0634514cd9699.pdf 073e8a4cc51b4d6fb7972693f205b20f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Intelligence in the Agri-Food System: Rethinking Sustainable Business Models in the COVID-19 Scenario Creator An entity primarily responsible for making the resource Assunta Di Vaio, Flavio Boccia, Loris Landriani, Rosa Palladino Description An account of the resource The aim of the paper is to investigate the artificial intelligence (AI) function in agri-food industry, as well as the role of stakeholders in its supply chain. Above all, from the beginning of the new millennium, scholars and practitioners have paid an increasing attention to artificial intelligence (AI) technologies in operational processes management and challenges for new business models, in a sustainable and socially responsible perspective. Thus, the stakeholders can assume a proactive or marginal role in the value creation for business, according to their own environmental awareness. These issues appear still “open” in some industries, such as the agri-food system, where the adoption of new technologies requires rethinking and redesigning the whole business model. Methodologically, we brought forward an in-depth review of the literature about major articles in this field. Especially, the study has been conducted following two phases: firstly, we extracted from scientific databases (Web of Science, Scopus, and Google Scholar) and studied relevant articles; secondly, we analyzed the selected articles. The findings highlight interesting issues about AI towards a “space economy” to achieve sustainable and responsible business models, also in the perspective of the COVID-19 pandemic scenario. Theoretical and managerial implications are discussed. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource literature review, Stakeholder, Environmental awareness, Environmental space, space economy Identifier An unambiguous reference to the resource within a given context 10.3390/su12124851 Source A related resource from which the described resource is derived Biotemas Publisher An entity responsible for making the resource available Universidade Federal de Santa Catarina Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Environmental effects of industries and plants, Renewable energy sources, Environmental sciences http://socictopen.socict.org/files/original/ecfb391282f0aa45d66593ca43c1e9ad.pdf b666216490a7fc861c57b5470a653d71 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2 Creator An entity primarily responsible for making the resource Manoharanehru Branavan, Wamadeva Balachandran, Anil Fernando, Mohammed A. Rohaim, Emily Clayton, Irem Sahin, Julianne Vilela, Manar E. Khalifa, Mohammad Q. Al-Natour, Mahmoud Bayoumi, Aurore C. Poirier, Mukunthan Tharmakulasingam, Nouman S. Chaudhry, Ravinder Sodi, Amy Brown, Peter Burkhart, Wendy Hacking, Judy Botham, Joe Boyce, Hayley Wilkinson, Craig Williams, Jayde Whittingham-Dowd, Elisabeth Shaw, Matt Hodges, Lisa Butler, Michelle D. Bates, Roberto La Ragione, Muhammad Munir Description An account of the resource Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Diagnosis, SARS-CoV-2, artificial intelligence, point-of-care, LAMP Identifier An unambiguous reference to the resource within a given context 10.3390/v12090972 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Microbiology http://socictopen.socict.org/files/original/6a618132880652f690784da973601a56.pdf 1fb0464287fd5ab53a0a28e9324d16cd Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-Like Pandemics: A Case Study for Early Flattening of the Curve Creator An entity primarily responsible for making the resource Burak Kantarci, Murat Şimşek Description An account of the resource The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9–30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource artificial intelligence, Public Health, epidemics, Pandemics, COVID-19, mobile assessment centers Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/ijerph17103437 Source A related resource from which the described resource is derived International Journal of Environmental Research and Public Health Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine http://socictopen.socict.org/files/original/b055d58dc3e8a280a87315d245ee3cdd.pdf 1fb0464287fd5ab53a0a28e9324d16cd Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-Like Pandemics: A Case Study for Early Flattening of the Curve Creator An entity primarily responsible for making the resource Burak Kantarci, Murat Simsek Description An account of the resource The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9–30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Public health, covid-19, epidemics, pandemics, artificial intelligence, mobile assessment centers Identifier An unambiguous reference to the resource within a given context 10.3390/ijerph17103437 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine http://socictopen.socict.org/files/original/d65050f344835ae344abc59248871069.pdf 5eadaea5526c25e022f9838fe54f730e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States Creator An entity primarily responsible for making the resource Abolfazl Mollalo, Kiara M. Rivera, Behzad Vahedi Description An account of the resource Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* (p < 0.05) in a geographic information system environment. The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource GIS, artificial neural networks, United States, Multi-layer perceptron, covid-19 coronavirus Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/ijerph17124204 Source A related resource from which the described resource is derived International Journal of Environmental Research and Public Health Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine http://socictopen.socict.org/files/original/d45ebf36d91b432363ab63bd7af128b3.pdf 9d8016a66cdf91b99140d1fe1f794827 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV) Creator An entity primarily responsible for making the resource Tomas Poblete, Samuel Ortega-Farías, Miguel Angel Moreno, Matthew Bardeen Description An account of the resource Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively. Date A point or period of time associated with an event in the lifecycle of the resource 2017 Subject The topic of the resource Artificial neural network, UAV, midday stem water potential, multispectral image processing Identifier An unambiguous reference to the resource within a given context 10.3390/s17112488 Source A related resource from which the described resource is derived Sensors Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chemical technology Relation A related resource <a href="https://www.mdpi.com/1424-8220/17/11/2488" target="_blank" rel="noreferrer noopener">https://www.mdpi.com/1424-8220/17/11/2488</a> http://socictopen.socict.org/files/original/a4cb76e46a1f0dc1c4b3166d2fa8b960.pdf 2003a788aed2a075e193f67e737e783c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artisanal Fisher Association Leaders’ Estimates of Poaching in Their Exclusive Access Management Areas Creator An entity primarily responsible for making the resource Rodrigo A. Estévez, Rodrigo A. Estévez, Stefan Gelcich, Stefan Gelcich, Pedro Romero, Pablo Romero Description An account of the resource In marine environments, poaching can become a key threat to marine ecosystem conservation. Poaching can occur in marine protected areas and/or in fishery management areas. Unfortunately, understanding the magnitude and characteristics of poaching under community based and co-management governance schemes in coastal and marine environments, has not received the attention it deserves. In Chile, a system of Territorial Users Rights for Fisheries (TURF) has been recognized as one of the largest experiences of small-scale fisheries co-management at a global scale. Currently, poaching is one of the main threats to the TURF system in Chile. In this article, we assessed poaching of a highly valuable benthic resource (Concholepas concholepas) from TURF management areas. We estimated artisanal fisher association leaders’ perceptions of poaching within their TURFs and explore determinants of poaching for Concholepas concholepas. Poaching of Concholepas concholepas showed differences along the studied sites. As expected, the greater abundance of Concholepas concholepas in the management areas generates an increased incentive to poach. Areas that make the greatest investment in surveillance are those most affected by poaching. However, our study cannot determine the effectiveness of current levels of surveillance on illegal extraction. Results show older areas tend to reduce the levels of illegal extraction, which could indicate a greater capacity and experience to control poaching. Supporting fisher associations in enforcing TURFs and following up on sanctions against perpetrators are conditioning factors, highlighted by fisher leaders, for TURF sustainability. The approach used in this study provides insights to prioritize geographies and opportunities to address poaching in small-scale co-managed fisheries. Date A point or period of time associated with an event in the lifecycle of the resource 2022 Subject The topic of the resource AMERBs, Concholepas concholepas, benthic, illegal fishing, traditional knowledge, turf Identifier An unambiguous reference to the resource within a given context 10.3389/fmars.2021.796518 Source A related resource from which the described resource is derived Frontiers in Marine Science Publisher An entity responsible for making the resource available Frontiers Media S.A. Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Science, General. Including nature conservation, geographical distribution Relation A related resource <a href="https://www.frontiersin.org/articles/10.3389/fmars.2021.796518/full" target="_blank" rel="noreferrer noopener">https://www.frontiersin.org/articles/10.3389/fmars.2021.796518/full</a> http://socictopen.socict.org/files/original/8cbc72f9c2db376a63f378f5f8148520.pdf 6091baf0063b2aba4c7f91f27ab3426d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artisanal production of Colonial cheese analyzed under Normative Instruction noº 30/2013 (municipalities in the Cantuquiriguaçu region, Paraná, Brazil) Creator An entity primarily responsible for making the resource Ionara Casali Tesser, Luciana Oliveira de Fariña, Luciana Bill Mikito Kottwitz, David Esteban Fariña Sosa, Débora Cristina Pramiu Description An account of the resource This study took place in 17 rural properties that produce colonial cheese in three towns of the Cantuquiriguaçu region (Paraná state, Brazil). A questionnaire was applied, in order to collect data on the milk and colonial cheese production, so that data could be analyzed according to the Normative Instruction nº 30/2013 from MAPA (Brazilian Ministry of Agriculture, Livestock and Food Supply). The results indicated that the visited farms did not have a certificate attesting them as free from brucellosis and tuberculosis (100%), nor even a program for mastitis control, nor good practices on milking and handling of cheese, not to mention poor control of water quality, poor pest control and minimum cheese ripening. Thus, according to the criteria from the Normative Instruction nº 30 / 2013, those farms were considered unsuitable for the production. It is worth noting, however, that one of the towns has already taken measures that partially met requirements of the legislation, indicating that the adoption of public policies and more technical support might assist producers to become apt for the adequate production of cheese from raw milk. Date A point or period of time associated with an event in the lifecycle of the resource 2017 Subject The topic of the resource Agricultura Familiar, Políticas Públicas, desenvolvimento rural, legislação Identifier An unambiguous reference to the resource within a given context 10.14295/2238-6416.v71i4.506 Source A related resource from which the described resource is derived Revista do Instituto de Latícinios Cândido Tostes Publisher An entity responsible for making the resource available Empresa de Pesquisa Agropecuária de Minas Gerais (EPAMIG) Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dairy processing. Dairy products Relation A related resource <a href="https://www.revistadoilct.com.br/rilct/article/view/506" target="_blank" rel="noreferrer noopener">https://www.revistadoilct.com.br/rilct/article/view/506</a>