Relationships among Environment, Climate, and Longevity in China
Bibliographic record
Abstract
Human longevity is influenced by environment and nutrition. We considered environmental and nutritional factors relating to longevity in Chinese cities. We found higher 85+/65+ distribution ratios, indicating enhanced longevity, in the coastal and southern regions of China. These areas also featured higher humidity, low standard deviation of monthly temperature, higher levels of selenium (Se) distribution in soil, and greater sea fish consumption. Moderate climate is more conducive to longevity, however, there is no significant difference in longevity between different sub-climatic types within moderate climate; the relation between humidity and longevity is not always positive, the relation between altitude and longevity is not always negative. Nutritional factors like Se and omega-3 fatty acids contained in sea fish were crucial to longevity. In contrast, the consumption of meat and freshwater fish were less related to longevity. Taken together, humidity, altitude, and per capita sea fish consumption, when evaluated via geographically weighted regression, explained 66% and 68% of longevity among Chinese individuals in 2000 and 2010, respectively. Other factors require further discussion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".