Place: Hoboken Publisher: Wiley WOS:000511221000001 ; International audience ; Wilderness areas offer unparalleled ecosystem conditions. However, growing human populations and consumption are among factors that drive encroachment on these areas. Here, we explore the threat of small-scale fisheries to wilderness reefs by developing a framework and modeling fluctuations in fishery range with fuel costs and fish prices. We modeled biomass of four fishery groups across the New Caledonian archipelago, and used fish and fuel prices from 2005 to 2020 to estimate the extent of exploited reefs across three fishing scenarios. From 2012 to 2018, maximum profitable range increased from 15 to over 30 hr from the capital city, expanding to reefs previously uneconomic to fish, including a UNESCO heritage site. By 2020, over half of New Caledonian (similar to 17% global) wilderness reefs will become profitable to fish. Our results demonstrate that remoteness from humans should not be considered protection for wilderness coral reefs in the context of rising fish prices.
Place: Hoboken Publisher: Wiley WOS:000511221000001 ; International audience ; Wilderness areas offer unparalleled ecosystem conditions. However, growing human populations and consumption are among factors that drive encroachment on these areas. Here, we explore the threat of small-scale fisheries to wilderness reefs by developing a framework and modeling fluctuations in fishery range with fuel costs and fish prices. We modeled biomass of four fishery groups across the New Caledonian archipelago, and used fish and fuel prices from 2005 to 2020 to estimate the extent of exploited reefs across three fishing scenarios. From 2012 to 2018, maximum profitable range increased from 15 to over 30 hr from the capital city, expanding to reefs previously uneconomic to fish, including a UNESCO heritage site. By 2020, over half of New Caledonian (similar to 17% global) wilderness reefs will become profitable to fish. Our results demonstrate that remoteness from humans should not be considered protection for wilderness coral reefs in the context of rising fish prices.
Place: Hoboken Publisher: Wiley WOS:000511221000001 ; International audience ; Wilderness areas offer unparalleled ecosystem conditions. However, growing human populations and consumption are among factors that drive encroachment on these areas. Here, we explore the threat of small-scale fisheries to wilderness reefs by developing a framework and modeling fluctuations in fishery range with fuel costs and fish prices. We modeled biomass of four fishery groups across the New Caledonian archipelago, and used fish and fuel prices from 2005 to 2020 to estimate the extent of exploited reefs across three fishing scenarios. From 2012 to 2018, maximum profitable range increased from 15 to over 30 hr from the capital city, expanding to reefs previously uneconomic to fish, including a UNESCO heritage site. By 2020, over half of New Caledonian (similar to 17% global) wilderness reefs will become profitable to fish. Our results demonstrate that remoteness from humans should not be considered protection for wilderness coral reefs in the context of rising fish prices.
Place: Hoboken Publisher: Wiley WOS:000511221000001 ; International audience ; Wilderness areas offer unparalleled ecosystem conditions. However, growing human populations and consumption are among factors that drive encroachment on these areas. Here, we explore the threat of small-scale fisheries to wilderness reefs by developing a framework and modeling fluctuations in fishery range with fuel costs and fish prices. We modeled biomass of four fishery groups across the New Caledonian archipelago, and used fish and fuel prices from 2005 to 2020 to estimate the extent of exploited reefs across three fishing scenarios. From 2012 to 2018, maximum profitable range increased from 15 to over 30 hr from the capital city, expanding to reefs previously uneconomic to fish, including a UNESCO heritage site. By 2020, over half of New Caledonian (similar to 17% global) wilderness reefs will become profitable to fish. Our results demonstrate that remoteness from humans should not be considered protection for wilderness coral reefs in the context of rising fish prices.
Place: Hoboken Publisher: Wiley WOS:000511221000001 ; International audience ; Wilderness areas offer unparalleled ecosystem conditions. However, growing human populations and consumption are among factors that drive encroachment on these areas. Here, we explore the threat of small-scale fisheries to wilderness reefs by developing a framework and modeling fluctuations in fishery range with fuel costs and fish prices. We modeled biomass of four fishery groups across the New Caledonian archipelago, and used fish and fuel prices from 2005 to 2020 to estimate the extent of exploited reefs across three fishing scenarios. From 2012 to 2018, maximum profitable range increased from 15 to over 30 hr from the capital city, expanding to reefs previously uneconomic to fish, including a UNESCO heritage site. By 2020, over half of New Caledonian (similar to 17% global) wilderness reefs will become profitable to fish. Our results demonstrate that remoteness from humans should not be considered protection for wilderness coral reefs in the context of rising fish prices.
Place: Hoboken Publisher: Wiley WOS:000511221000001 ; International audience ; Wilderness areas offer unparalleled ecosystem conditions. However, growing human populations and consumption are among factors that drive encroachment on these areas. Here, we explore the threat of small-scale fisheries to wilderness reefs by developing a framework and modeling fluctuations in fishery range with fuel costs and fish prices. We modeled biomass of four fishery groups across the New Caledonian archipelago, and used fish and fuel prices from 2005 to 2020 to estimate the extent of exploited reefs across three fishing scenarios. From 2012 to 2018, maximum profitable range increased from 15 to over 30 hr from the capital city, expanding to reefs previously uneconomic to fish, including a UNESCO heritage site. By 2020, over half of New Caledonian (similar to 17% global) wilderness reefs will become profitable to fish. Our results demonstrate that remoteness from humans should not be considered protection for wilderness coral reefs in the context of rising fish prices.
IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...
IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...
IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...
IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...
IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...
IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...
IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...
