Differentially expressed genes (DEGs)^a showing the greatest fold changes from each potato tissue: 10 with greatest increase in expression and 10 with greatest decrease in expression. by Margaret A. Carpenter (6104180)

“…<p>Differentially expressed genes (DEGs)^a showing the greatest fold changes from each potato tissue: 10 with greatest increase in expression and 10 with greatest decrease in expression.…”

Wetland classification using the ensemble simulations. by Nic Gedney (19762301)

Tissue, days post-infection (dpi) and the top 10 most significant genes with increased and decreased expression with valid gene symbols for the response contrasts. by Gillian P. McHugo (8965919)

Selection frequencies plotted in decreasing order. by Hannah P. Rowe (7830599)

Wetland classification scheme for the contemporary period. by Nic Gedney (19762301)

Decadal mean regional hydrology. by Nic Gedney (19762301)

Decadal seasonality of the hydrological cycle over the wetland regions. by Nic Gedney (19762301)

Simulated regional mean wetland seasons. by Nic Gedney (19762301)

Innovation, nightlights and a continuous measure of city size. by Saul Estrin (8629173)

SHAP waterfall plot. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

SHAP decision plot. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

SHAP dependence plots. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

Projected decadal changes in seasonality in the hydrological cycle over the wetland regions. by Nic Gedney (19762301)

Contemporary mean inundation fraction in the Amazon basin. by Nic Gedney (19762301)

Regional contemporary (1990–1999 mean) precipitation and JULES inundation fraction over the Amazon basin. by Nic Gedney (19762301)

Changes in decadal precipitation and JULES inundation between 1990–1999 and 2089–2098. by Nic Gedney (19762301)

The contemporary, long-term seasonality in inundation over the whole of the Amazon basin. by Nic Gedney (19762301)

Percentage regional changes in decadal regional hydrology. by Nic Gedney (19762301)

Wetlands over the lowland Amazon basin from the Hess product [19,20]. by Nic Gedney (19762301)

SHAP dependence plots with interaction coloring. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”