Showing 1 - 20 results of 6,820 for search '(((( auc values decrease ) OR ( c values increased ))) OR ( _ largest decrease ))*', query time: 0.47s Refine Results
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    ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

    Published 2024
    “…As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. …”
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    TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5<sup>th</sup> and 95<sup>th</sup> percentile change points are determined from 500 bootstrap replicate runs.... by Brent J. Bellinger (21156150)

    Published 2025
    “…<p>TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5<sup>th</sup> and 95<sup>th</sup> percentile change points are determined from 500 bootstrap replicate runs. …”
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    Sequential Abbott S/C values. by Neal Alexander (18417)

    Published 2025
    “…Dark blue points, with first S/C ≥0.49, are defined as positive on the first donation, with any increase to the second donation being less than the threshold for fold change. …”
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    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)

    Published 2025
    “…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>…”
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