The AUC values of different sampling methods in the cumulative sample size. by Xi Li (19208)

ROC curves and corresponding AUC values for different models in the training set. by Yanxuan Wu (9623598)

Data such as specificity, sensitivity and AUC under different cut-off values. by Wenyan Xiao (2737087)

The tree’s accuracy, AUC, sensitivity, and specificity values on the train and test data. by Biche Osong (14716771)

AUCs of four inflammation indexes for discriminating CAD. by Tong Sun (1564231)

TITAN decreaser macroinvertebrate heatmap Heatmap of sensitive (i.e., <i>z-</i>) macroinvertebrate taxa (y-axis) to at least one of the five stressors, in decreasing order of numbe... by Brent J. Bellinger (21156150)

This figure presents the ROC curves and corresponding AUC values for various ML models. by Yanxuan Wu (9623598)

TITAN decreaser diatom heatmap. by Brent J. Bellinger (21156150)

Median and IQR of the model AUC values across each study site for internal and external validation. by Ioana Duta (18462981)

Table of values for <i>p.</i> by Long Di (9977453)

Model AUC score as a function of the number of estimators (RF trees). by Julia Livermore (17098084)

Model AUC score as a function of the maximum decision tree depth. by Julia Livermore (17098084)

AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre</p>…”

<i>r</i>² values for <i>α</i> relationships. by James Cushway (19942288)

The multiple criteria qualitative value-based pricing framework “MARIE” for new clinical status by Akina Takami (16407200)

TITAN increaser macroinvertebrate heatmap Heatmap of tolerant (i.e., <i>z</i>+) macroinvertebrate taxa (y-axis) to at least one of the five stressors, in decreasing order of number... by Brent J. Bellinger (21156150)

Lymphocytes decreased in mouse pneumonia model. by Qi Xu (134661)

Decreased <i>Notch3</i> asymmetry in <i>Nodal</i> mutants. by Tobias Holm Bønnelykke (20968460)

The values calculated for the inhibitor dissociation constant, as detailed in Eqs 10 and 11 and approximated values for the binding rate constants of the inhibitors. by J. Roadnight Sheehan (20141887)

SHAP Value Analysis for Model Prediction. by Youli Jiang (15249070)

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