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

Relationship between monthly NDVI and climatic variables values (Tmax, maximum air temperature; P, precipitation; VPD, vapor pressure deficit), in areas with increasing trends (<i>p</i> < 0.05). by Oswaldo Maillard (20660661)

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^th and 95^th percentile change points are determined from 500 bootstrap replicate runs.... by Brent J. Bellinger (21156150)

“…<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^th and 95^th percentile change points are determined from 500 bootstrap replicate runs. …”

Moran’s I value, Z value and P value of CCD-GFEE. by Dalai Ma (17847101)

“…Within them, the CCD of Chengdu is the highest, Chongqing has achieved the largest stage leap. (4) The global Moran’s I consistently remained positive and exhibited a tendency of initially rising and subsequently falling, indicating that the spatial aggregation effect of CCD-GFEE first increased and then decreased. (5) The CCD-GFEE driving factors are examined using the spatial econometric model, and it has been observed that the impact of population size and government intervention on CCD-GFEE is negative, while the impact of industrial structure, technological progress and economic level on the coupling and coordination of CCD-GFEE is positive. …”

Relationship between monthly NDVI and climatic variables values (Tmax, maximum air temperature; P, precipitation; VPD, vapor pressure deficit), in areas with decreasing trends (<i>... by Oswaldo Maillard (20660661)

Seasonal spatial-temporal trend of NDVI in burned areas across Africa (2001–2020). Percentages (%) of increases or decreases (%) based on the Mann-Kendall test and statistically significant (<i>p</i> < 0.05) and non-significant (p > 0.05) values. by Oswaldo Maillard (20660661)

Proportion of the increase (green) or decrease (red) in forest cover in areas with different fire recurrences in Africa, according to the Mann-Kendall test and statistically signif... by Oswaldo Maillard (20660661)

The correlation coefficient (<i>r</i>) between NDVI values and the seasonal series (2001–2020) of maximum air temperature (Tmax), precipitation (P), and vapor pressure deficit (VPD... by Oswaldo Maillard (20660661)

L* Value Increases with Increasing Zinc Oxide Percentage. by Alexandra M. Maldonado López (22127340)

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. …”

A visual comparison of the associated resistance data from Table 1 with the values of <i>k</i>_cat, <i>K</i>_M, catalytic efficiency (<i>k</i>_cat/<i>... by J. Roadnight Sheehan (20141887)

Yearly crude rates normalized by their 1999 value. by Tony Wong (779773)

“…<p>Values below 1 indicate crude rate decreases relative to their 1999 value, those above 1 indicate increases. …”

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

Subjects:

Change in computation time for an increase in number of nodes <i>N</i> and number of people in system <i>K</i> (<i>C</i> = 3). by Shinya Mizuno (3689788)

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

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>…”

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

Carbon (<i>δ</i>¹³C) and nitrogen (<i>δ</i>¹⁵N) isotope data from bone collagen from archaeological fauna and modern plants in the isthmus area, including Cos... by Ashley E. Sharpe (8681484)

<i>M</i>. <i>separata</i> herbivory primes systemic leaves for increased JA and JA-Ile. by Bo Fan (383210)

Individual values for figures. by Robert S. Leigh (10329847)