Familial and Environmental Influences on Longevity in a Pre-industrial Population
Bibliographic record
Abstract
Data from historical populations provide an adequate context for the examination of the familial and environmental components of longevity. We have investigated the relation between sibling survivorship and longevity through French-Canadian children of a completed fertility cohort born between 1625 and 1704. The Cox regression model was used to analyze the effects of sibling survivorship on the survival time of these early Canadian inhabitants. Other covariates such as regional variation, secular trends (i.e. period effects), parental and spousal survival were also taken into consideration. Our findings show that individuals with at least one sibling surviving beyond 85 years of age had a life-long sustained mortality advantage over the general population. The risks of death after age 50 was 55% and 60% lower for females and males, respectively, having a long-lived sibling. In comparison, the parental of origin effects were negligible. Only the mother-son association in age at death was found significant among the four possible parent-child pairs. Overall, the various models provided better fit to male than to female data. Biological as well as social explanations are explored in order to account for the various results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".