Earth & Environmental Sciences

48 items

Annals of Computer Science and Information Systems Sep 25, 2026
The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.
The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.
A PCA-plus-local-differential-privacy framework for agricultural data sharing trades about 5.33% average accuracy loss for privacy on real datasets
The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.
The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.
RAS Techniques and Instruments Sep 25, 2026
The work feeds self-supervised BYOL features extracted from source cutouts into the Astronomaly: Protege active-learning framework and evaluates the pipeline on high-resolution (about 7 arcsec), convolved (15 arcsec), and concatenated-feature MeerKAT Galaxy Cluster Legacy Survey (MGCLS) datasets, using tracers from a human-labelled catalogue as both guidance and benchmark; high-resolution features identify diffuse sources earlier than convolved ones, concatenated features perform best overall, and of the top 100 sources ranked by Protege 99 exhibit some form of diffuse radio emission with 55 confirmed as cluster-related, recovering 55 of 121 tracers from 62,587 sources with only 300 human labels.
The work feeds self-supervised BYOL features extracted from source cutouts into the Astronomaly: Protege active-learning framework and evaluates the pipeline on high-resolution (about 7 arcsec), convolved (15 arcsec), and concatenated-feature MeerKAT Galaxy Cluster Legacy Survey (MGCLS) datasets, using tracers from a human-labelled catalogue as both guidance and benchmark; high-resolution features identify diffuse sources earlier than convolved ones, concatenated features perform best overall, and of the top 100 sources ranked by Protege 99 exhibit some form of diffuse radio emission with 55 confirmed as cluster-related, recovering 55 of 121 tracers from 62,587 sources with only 300 human labels.
BYOL features plus Protege active learning rank 100 MGCLS candidates, 99 showing diffuse radio characteristics and 55 confirmed as cluster-related emission
The work feeds self-supervised BYOL features extracted from source cutouts into the Astronomaly: Protege active-learning framework and evaluates the pipeline on high-resolution (about 7 arcsec), convolved (15 arcsec), and concatenated-feature MeerKAT Galaxy Cluster Legacy Survey (MGCLS) datasets, using tracers from a human-labelled catalogue as both guidance and benchmark; high-resolution features identify diffuse sources earlier than convolved ones, concatenated features perform best overall, and of the top 100 sources ranked by Protege 99 exhibit some form of diffuse radio emission with 55 confirmed as cluster-related, recovering 55 of 121 tracers from 62,587 sources with only 300 human labels.
The work feeds self-supervised BYOL features extracted from source cutouts into the Astronomaly: Protege active-learning framework and evaluates the pipeline on high-resolution (about 7 arcsec), convolved (15 arcsec), and concatenated-feature MeerKAT Galaxy Cluster Legacy Survey (MGCLS) datasets, using tracers from a human-labelled catalogue as both guidance and benchmark; high-resolution features identify diffuse sources earlier than convolved ones, concatenated features perform best overall, and of the top 100 sources ranked by Protege 99 exhibit some form of diffuse radio emission with 55 confirmed as cluster-related, recovering 55 of 121 tracers from 62,587 sources with only 300 human labels.
Journal of Computational Science Sep 25, 2026
This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.
This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.
Reinforcement learning learns Leith coefficients online so coarse 2D turbulence simulations reproduce extreme vorticity events
This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.
This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.
Eos Sep 24, 2026
Gaglioti et al. used remotely sensed data on Alaskan wildfires between 1984 and 2020 to examine fires that encounter previously burned areas and applied a logistic regression model to assess how strongly young fuels resist wildfire and whether warmer, drier conditions affect that resistance, finding that young vegetation in recently burned areas has historically exerted a strong negative influence on fire activity, with reburning rates 1 to 3 orders of magnitude lower than in older forests and burned-area perimeters often acting as barriers to later fires; a simple landscape burning model estimates that without this negative feedback Alaska would have seen 5 times more wildfires over the past 40 years; extreme fire weather significantly dampened this relationship, especially in younger for
Gaglioti et al. used remotely sensed data on Alaskan wildfires between 1984 and 2020 to examine fires that encounter previously burned areas and applied a logistic regression model to assess how strongly young fuels resist wildfire and whether warmer, drier conditions affect that resistance, finding that young vegetation in recently burned areas has historically exerted a strong negative influence on fire activity, with reburning rates 1 to 3 orders of magnitude lower than in older forests and burned-area perimeters often acting as barriers to later fires; a simple landscape burning model estimates that without this negative feedback Alaska would have seen 5 times more wildfires over the past 40 years; extreme fire weather significantly dampened this relationship, especially in younger for
Remote sensing of Alaskan fires from 1984 to 2020 shows past burn scars cut reburning rates by 1 to 3 orders of magnitude, and without this negative feedback the region would have seen 5 times more wildfires
Gaglioti et al. used remotely sensed data on Alaskan wildfires between 1984 and 2020 to examine fires that encounter previously burned areas and applied a logistic regression model to assess how strongly young fuels resist wildfire and whether warmer, drier conditions affect that resistance, finding that young vegetation in recently burned areas has historically exerted a strong negative influence on fire activity, with reburning rates 1 to 3 orders of magnitude lower than in older forests and burned-area perimeters often acting as barriers to later fires; a simple landscape burning model estimates that without this negative feedback Alaska would have seen 5 times more wildfires over the past 40 years; extreme fire weather significantly dampened this relationship, especially in younger for
Gaglioti et al. used remotely sensed data on Alaskan wildfires between 1984 and 2020 to examine fires that encounter previously burned areas and applied a logistic regression model to assess how strongly young fuels resist wildfire and whether warmer, drier conditions affect that resistance, finding that young vegetation in recently burned areas has historically exerted a strong negative influence on fire activity, with reburning rates 1 to 3 orders of magnitude lower than in older forests and burned-area perimeters often acting as barriers to later fires; a simple landscape burning model estimates that without this negative feedback Alaska would have seen 5 times more wildfires over the past 40 years; extreme fire weather significantly dampened this relationship, especially in younger for
Terence Tao blog RSS Sep 24, 2026
Using OpenAI's September 8 announcement of a finite-time blow-up proof for the forced Navier-Stokes equation as an entry point, Tapio Schneider distinguishes episteme (explanatory understanding) from techne (the craft of prediction), and argues that when predictions must be trusted before they can be empirically verified—as in decadal climate projection or the design of a novel aircraft—trust comes from an auditable causal chain running from assumptions and input data to outcomes, each link of which can be tested individually; AI should therefore be embedded in auditable scaffolds such as physical conservation laws and used to learn closure models that can be checked against high-resolution simulations, observations, or experiments, rather than deployed as end-to-end models.
Using OpenAI's September 8 announcement of a finite-time blow-up proof for the forced Navier-Stokes equation as an entry point, Tapio Schneider distinguishes episteme (explanatory understanding) from techne (the craft of prediction), and argues that when predictions must be trusted before they can be empirically verified—as in decadal climate projection or the design of a novel aircraft—trust comes from an auditable causal chain running from assumptions and input data to outcomes, each link of which can be tested individually; AI should therefore be embedded in auditable scaffolds such as physical conservation laws and used to learn closure models that can be checked against high-resolution simulations, observations, or experiments, rather than deployed as end-to-end models.
Schneider uses the Navier-Stokes finite-time blow-up proof to argue that AI predictions can be trusted only inside an auditable causal chain
Using OpenAI's September 8 announcement of a finite-time blow-up proof for the forced Navier-Stokes equation as an entry point, Tapio Schneider distinguishes episteme (explanatory understanding) from techne (the craft of prediction), and argues that when predictions must be trusted before they can be empirically verified—as in decadal climate projection or the design of a novel aircraft—trust comes from an auditable causal chain running from assumptions and input data to outcomes, each link of which can be tested individually; AI should therefore be embedded in auditable scaffolds such as physical conservation laws and used to learn closure models that can be checked against high-resolution simulations, observations, or experiments, rather than deployed as end-to-end models.
Using OpenAI's September 8 announcement of a finite-time blow-up proof for the forced Navier-Stokes equation as an entry point, Tapio Schneider distinguishes episteme (explanatory understanding) from techne (the craft of prediction), and argues that when predictions must be trusted before they can be empirically verified—as in decadal climate projection or the design of a novel aircraft—trust comes from an auditable causal chain running from assumptions and input data to outcomes, each link of which can be tested individually; AI should therefore be embedded in auditable scaffolds such as physical conservation laws and used to learn closure models that can be checked against high-resolution simulations, observations, or experiments, rather than deployed as end-to-end models.
npj Sustainable Agriculture Sep 24, 2026
Using soil moisture from 17 CMIP6 models bias-corrected against GLDAS and combined into a multi-model ensemble, the study characterizes 2015–2050 global soil moisture droughts with a non-parametric SSMI under SSP2-4.5 and SSP5-8.5 and assesses agricultural exposure with a Drought Exposure Index overlaying cropland, finding intensified drought characteristics toward mid-century, longer durations and wider spatial extent under SSP5-8.5, pronounced drying in South America, southern Europe, South Asia and parts of North America, and a global mean DEI rising from 0.71 under SSP2-4.5 to 0.78 under SSP5-8.5.
Using soil moisture from 17 CMIP6 models bias-corrected against GLDAS and combined into a multi-model ensemble, the study characterizes 2015–2050 global soil moisture droughts with a non-parametric SSMI under SSP2-4.5 and SSP5-8.5 and assesses agricultural exposure with a Drought Exposure Index overlaying cropland, finding intensified drought characteristics toward mid-century, longer durations and wider spatial extent under SSP5-8.5, pronounced drying in South America, southern Europe, South Asia and parts of North America, and a global mean DEI rising from 0.71 under SSP2-4.5 to 0.78 under SSP5-8.5.
Bias-corrected 17-model CMIP6 ensemble projects global agricultural drought exposure rising from 0.71 to 0.78 by 2050, with South Asia, Southern Africa, South America and southern Europe croplands most at risk
Using soil moisture from 17 CMIP6 models bias-corrected against GLDAS and combined into a multi-model ensemble, the study characterizes 2015–2050 global soil moisture droughts with a non-parametric SSMI under SSP2-4.5 and SSP5-8.5 and assesses agricultural exposure with a Drought Exposure Index overlaying cropland, finding intensified drought characteristics toward mid-century, longer durations and wider spatial extent under SSP5-8.5, pronounced drying in South America, southern Europe, South Asia and parts of North America, and a global mean DEI rising from 0.71 under SSP2-4.5 to 0.78 under SSP5-8.5.
Using soil moisture from 17 CMIP6 models bias-corrected against GLDAS and combined into a multi-model ensemble, the study characterizes 2015–2050 global soil moisture droughts with a non-parametric SSMI under SSP2-4.5 and SSP5-8.5 and assesses agricultural exposure with a Drought Exposure Index overlaying cropland, finding intensified drought characteristics toward mid-century, longer durations and wider spatial extent under SSP5-8.5, pronounced drying in South America, southern Europe, South Asia and parts of North America, and a global mean DEI rising from 0.71 under SSP2-4.5 to 0.78 under SSP5-8.5.
Frontiers in Animal Science Sep 24, 2026
Using 20 eighteen-month-old pregnant crossbred replacement heifers averaging 383 kg, this study fed 0.0%, 0.5%, 1.0%, and 2.0% North Atlantic brown seaweed (Atlantic GRO®, made of Laminaria longicruris and Fucus vesiculosus) on a dry matter basis over a 50-day performance trial, then measured gas emissions in 16 of them with headbox metabolic chambers for two 24-h periods; no supplementation level adversely affected growth performance (p > 0.51), while the 1.0% and 2.0% groups emitted about 8.2% and 8.7% less methane than controls (p < 0.05), with significantly lower carbon dioxide emissions and oxygen consumption as well (p < 0.05).
Using 20 eighteen-month-old pregnant crossbred replacement heifers averaging 383 kg, this study fed 0.0%, 0.5%, 1.0%, and 2.0% North Atlantic brown seaweed (Atlantic GRO®, made of Laminaria longicruris and Fucus vesiculosus) on a dry matter basis over a 50-day performance trial, then measured gas emissions in 16 of them with headbox metabolic chambers for two 24-h periods; no supplementation level adversely affected growth performance (p > 0.51), while the 1.0% and 2.0% groups emitted about 8.2% and 8.7% less methane than controls (p < 0.05), with significantly lower carbon dioxide emissions and oxygen consumption as well (p < 0.05).
Adding 1.0% and 2.0% North Atlantic brown seaweed to pregnant replacement heifers cut methane emissions by about 8.2% and 8.7% without affecting growth performance
Using 20 eighteen-month-old pregnant crossbred replacement heifers averaging 383 kg, this study fed 0.0%, 0.5%, 1.0%, and 2.0% North Atlantic brown seaweed (Atlantic GRO®, made of Laminaria longicruris and Fucus vesiculosus) on a dry matter basis over a 50-day performance trial, then measured gas emissions in 16 of them with headbox metabolic chambers for two 24-h periods; no supplementation level adversely affected growth performance (p > 0.51), while the 1.0% and 2.0% groups emitted about 8.2% and 8.7% less methane than controls (p < 0.05), with significantly lower carbon dioxide emissions and oxygen consumption as well (p < 0.05).
Using 20 eighteen-month-old pregnant crossbred replacement heifers averaging 383 kg, this study fed 0.0%, 0.5%, 1.0%, and 2.0% North Atlantic brown seaweed (Atlantic GRO®, made of Laminaria longicruris and Fucus vesiculosus) on a dry matter basis over a 50-day performance trial, then measured gas emissions in 16 of them with headbox metabolic chambers for two 24-h periods; no supplementation level adversely affected growth performance (p > 0.51), while the 1.0% and 2.0% groups emitted about 8.2% and 8.7% less methane than controls (p < 0.05), with significantly lower carbon dioxide emissions and oxygen consumption as well (p < 0.05).
SIAM Journal on Applied Mathematics Sep 24, 2026
The work develops an ensemble score filter (EnSF) that integrates image inpainting to address data assimilation with partial observations: at each filtering step a training-free diffusion model estimates the observed states by incorporating likelihood information into the score function, and image inpainting methods then predict the unobserved state variables, with performance demonstrated by tracking Surface Quasi-Geostrophic (SQG) model dynamics across a variety of scenarios as a proof of concept.
The work develops an ensemble score filter (EnSF) that integrates image inpainting to address data assimilation with partial observations: at each filtering step a training-free diffusion model estimates the observed states by incorporating likelihood information into the score function, and image inpainting methods then predict the unobserved state variables, with performance demonstrated by tracking Surface Quasi-Geostrophic (SQG) model dynamics across a variety of scenarios as a proof of concept.
Integrating image inpainting into an ensemble score filter to track surface quasi-geostrophic dynamics under partial observations
The work develops an ensemble score filter (EnSF) that integrates image inpainting to address data assimilation with partial observations: at each filtering step a training-free diffusion model estimates the observed states by incorporating likelihood information into the score function, and image inpainting methods then predict the unobserved state variables, with performance demonstrated by tracking Surface Quasi-Geostrophic (SQG) model dynamics across a variety of scenarios as a proof of concept.
The work develops an ensemble score filter (EnSF) that integrates image inpainting to address data assimilation with partial observations: at each filtering step a training-free diffusion model estimates the observed states by incorporating likelihood information into the score function, and image inpainting methods then predict the unobserved state variables, with performance demonstrated by tracking Surface Quasi-Geostrophic (SQG) model dynamics across a variety of scenarios as a proof of concept.
Machine Learning Earth Sep 24, 2026
The study introduces ArchesClimate-SSP (AC-SSP), built on the ArchesWeather architecture and trained with an energy-score loss plus a variogram loss while conditioning on forcing concentrations (CO2, CH4, N2O, six aerosol species, ozone), and trained on IPSL-CM6A-LR monthly data for 2015-2100 to autoregressively generate SSP scenarios unseen in training; on the held-out overshoot scenario SSP5-3.4 its 2090-2100 land surface temperature RMSE is 0.9739 K, lower than the statistical emulator MESMER-M's 1.0963 K, and its interannual variability of 0.6050 is closer to the IPSL target's 0.5166 than MESMER-M's 0.6375.
The study introduces ArchesClimate-SSP (AC-SSP), built on the ArchesWeather architecture and trained with an energy-score loss plus a variogram loss while conditioning on forcing concentrations (CO2, CH4, N2O, six aerosol species, ozone), and trained on IPSL-CM6A-LR monthly data for 2015-2100 to autoregressively generate SSP scenarios unseen in training; on the held-out overshoot scenario SSP5-3.4 its 2090-2100 land surface temperature RMSE is 0.9739 K, lower than the statistical emulator MESMER-M's 1.0963 K, and its interannual variability of 0.6050 is closer to the IPSL target's 0.5166 than MESMER-M's 0.6375.
ArchesClimate-SSP, trained with an energy-score loss, reaches 0.9739 K land-temperature RMSE on unseen SSP5-3.4, beating MESMER-M's 1.0963 K
The study introduces ArchesClimate-SSP (AC-SSP), built on the ArchesWeather architecture and trained with an energy-score loss plus a variogram loss while conditioning on forcing concentrations (CO2, CH4, N2O, six aerosol species, ozone), and trained on IPSL-CM6A-LR monthly data for 2015-2100 to autoregressively generate SSP scenarios unseen in training; on the held-out overshoot scenario SSP5-3.4 its 2090-2100 land surface temperature RMSE is 0.9739 K, lower than the statistical emulator MESMER-M's 1.0963 K, and its interannual variability of 0.6050 is closer to the IPSL target's 0.5166 than MESMER-M's 0.6375.
The study introduces ArchesClimate-SSP (AC-SSP), built on the ArchesWeather architecture and trained with an energy-score loss plus a variogram loss while conditioning on forcing concentrations (CO2, CH4, N2O, six aerosol species, ozone), and trained on IPSL-CM6A-LR monthly data for 2015-2100 to autoregressively generate SSP scenarios unseen in training; on the held-out overshoot scenario SSP5-3.4 its 2090-2100 land surface temperature RMSE is 0.9739 K, lower than the statistical emulator MESMER-M's 1.0963 K, and its interannual variability of 0.6050 is closer to the IPSL target's 0.5166 than MESMER-M's 0.6375.
Eos Sep 23, 2026
Henderson et al. applied a Bayesian maximum a posteriori (MAP) method to decompose data collected over 60 days by a network of pressure and velocity sensors at Torrey Pines State Beach in California, in order to separate the contributions of different types of infragravity waves to wave run-up, and found that edge waves running parallel to the shoreline account for roughly 28% of the infragravity wave energy.
Henderson et al. applied a Bayesian maximum a posteriori (MAP) method to decompose data collected over 60 days by a network of pressure and velocity sensors at Torrey Pines State Beach in California, in order to separate the contributions of different types of infragravity waves to wave run-up, and found that edge waves running parallel to the shoreline account for roughly 28% of the infragravity wave energy.
Henderson et al. decomposed nearshore infragravity waves with a Bayesian maximum a posteriori method and measured edge waves at roughly 28% of infragravity wave energy
Henderson et al. applied a Bayesian maximum a posteriori (MAP) method to decompose data collected over 60 days by a network of pressure and velocity sensors at Torrey Pines State Beach in California, in order to separate the contributions of different types of infragravity waves to wave run-up, and found that edge waves running parallel to the shoreline account for roughly 28% of the infragravity wave energy.
Henderson et al. applied a Bayesian maximum a posteriori (MAP) method to decompose data collected over 60 days by a network of pressure and velocity sensors at Torrey Pines State Beach in California, in order to separate the contributions of different types of infragravity waves to wave run-up, and found that edge waves running parallel to the shoreline account for roughly 28% of the infragravity wave energy.

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