The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)

Analysis of tea output value distribution and growth- decrease rate of provinces. by Kun Qin (177235)

ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. …”

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)

“…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>…”

Area under the curve (AUC) for external validation performance. by Junichi Kushioka (12236447)

Largest changes in influenza vaccine rates, Pre- vs. Post-COVID-19. by Tyler B. Nofzinger (21560244)

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Chun Fai Martin Cheng: How can we decrease metabolism in the New Zealand green-lipped mussels, Perna canaliculus, for better transport? by Cheng Martin (11717816)

The experimental set-up we used. by Hillary Jean-Joseph (9676166)

Overview of the WeARTolerance program. by Ana Beato (20489933)

“…This research evaluates the "WeARTolerance’’ arts-based program in reducing mental health stigma among diverse youths. …”

AUC of various models to predict imeglimin efficacy for liver function evaluated by ROC curves. by Naruyoshi Gyoutoku (22537831)

Column charts of WiAUC in therapy and control group at baseline and follow-up. by Felix L. Herr (21544362)

Side angle tea picking. by Chunhua Yang (346871)

“…<div><p>This study proposes the S-YOLOv10-ASI algorithm to improve the accuracy of tea identification and harvesting by robots, integrating a slice-assisted super-reasoning technique. …”

The top 10 putative metabolites with the largest negative fold change between normal and thin cows. by Bhuripit Saraphol (20855573)

The top 10 putative metabolites with the largest positive fold change between normal and thin cows. by Bhuripit Saraphol (20855573)

The soil bacterial community richness and diversity in different tea varieties root zone soil. by Jun Sun (48981)

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LEfSe analysis of root zone soil bacterium from different tea varieties. by Jun Sun (48981)

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AUC ROC curve. by Dieuwke Luijten (15282736)

“…</p><p>Results</p><p>During follow-up, 28 patients had residual PH (42%). A decrease in VG-RVPO after PEA was associated with decrease in mPAP or indexed RV mass post PEA (r = 0.55, p < 0.05 and r = 0.64, p < 0.05, respectively). …”

Application of WeChat-based cognitive behavioural stress management for early-stage cervical cancer patients: a randomised controlled study by Qing Hao (566198)

“…<p>This randomised controlled study was aimed at investigating the effects of WeChat-based cognitive behavioural stress management (WB-CBSM) on the mental health of patients with early-stage cervical cancer treated with surgical resection.…”

Summary statistics of key variables. by Saul Estrin (8629173)

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Innovation and night light in large and small cities. by Saul Estrin (8629173)

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