Additional file 1 of Temperature variability and common diseases of the elderly in China: a national cross-sectional study
Bibliographic record
Abstract
Additional file 1: Fig. S1. Scatter plot of relationship between TV in 2010–14 and prevalence of diseases and conditions in China. TV, temperature variability. Fig. S2. Scatter plot of relationship between TV in 2011–14 and prevalence of diseases and conditions in China. TV, temperature variability. 4. Fig. S3. Scatter plot of relationship between TV in 2012–14 and prevalence of diseases and conditions in China. TV, temperature variability. Fig. S4. Scatter plot of relationship between TV in 2013–14 and prevalence of diseases and conditions in China. TV, temperature variability. Fig. S5. Non-linear dose-response relationship between TV in 2010–14 and diseases and conditions in China. TV, temperature variability. Fig. S6. Non-linear dose-response relationship between TV in 2011–14 and diseases and conditions in China. TV, temperature variability. Fig. S7. Non-linear dose-response relationship between TV in 2012–14 and diseases and conditions in China. TV, temperature variability. Fig. S8. Non-linear dose-response relationship between TV in 2013–14 and diseases and conditions in China. TV, temperature variability. Fig. S9. Non-linear dose-response relationship between TV in 2014 and diseases and conditions in China. TV, temperature variability. Table S1. List of monitoring stations in 181 cities of 30 provinces. Table S2. Risk (Odds ratio, OR) for all diseases associated with every 1 °C increase in TV during 2014. Table S3. Risk (Odds ratio, OR) for all diseases associated with every 1 °C increase in TV during 2010–2014. Table S4. Risk (Odds ratio, OR) for all diseases associated with every 1 °C increase in TV during 2011–2014. Table S5. Risk (Odds ratio, OR) for all diseases associated with every 1 °C increase in TV during 2012–2014. Table S6. Risk (Odds ratio, OR) for all diseases associated with every 1 °C increase in TV during 2013–2014. Table S7. Results of sensitivity analyses for TV 2014 using different df for mean temperature and mean relative humidity. Table S8. Results of sensitivity analyses for TV 2010–2014 using different df for mean temperature and mean relative humidity. Table S9. Results of sensitivity analyses for TV 2011–2014 using different df for mean temperature and mean relative humidity. Table S10. Results of sensitivity analyses for TV 2012–2014 using different df for mean temperature and mean relative humidity. Table S11. Results of sensitivity analyses for TV 2013–2014 using different df for mean temperature and mean relative humidity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.387 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".