Confidence intervals for physiological regions. by Inmaculada Villanueva-Baxarias (21454246)

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Knowledge regarding osteoporosis among the study participants. by Surakshya Khanal (19949745)

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This figure shows the forest plot for the multivariable logistic regression found in Table 1. by Scott J. Halperin (17835402)

A sample choice question without a warning label. by Yulian Ding (5984591)

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Description of the warning label. by Yulian Ding (5984591)

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The effectiveness of a warning label on carbonated beverage consumption for consumers with different habit strengths. by Yulian Ding (5984591)

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The impact of a warning label on carbonated beverage consumption. by Yulian Ding (5984591)

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Flow chart of the study. by Yulian Ding (5984591)

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A sample choice question with a warning label. by Yulian Ding (5984591)

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Consumption frequencies of different types of SSBs. by Yulian Ding (5984591)

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Summary statistics of the sample. by Yulian Ding (5984591)

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The impact of health knowledge and obesity status on the effectiveness of a warning label. by Yulian Ding (5984591)

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Product attributes and attribute levels. by Yulian Ding (5984591)

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Dataset used in this study. by Yulian Ding (5984591)

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Socio-demographics of the subsamples with different habit strengths. by Yulian Ding (5984591)

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Definitions of the variables. by Yulian Ding (5984591)

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Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”