Bibliographic record
Abstract
With the remarkable decline in mortality among the elderly after the second half of the twentieth century, the number of centenarians has increased spectacularly in low-mortality countries. In this era of human life extension, it is becoming increasingly important to obtain precise mortality at very advanced ages. We use an exhaustive set of data on all French-Canadian centenarians born in Quebec between 1870 and 1896 and who died in Quebec between the 1970-2009 to shed new light on the age-trajectory of mortality beyond 100 years, which remains uncertain and results in significant scientific debates. Thanks to Quebec’s parish registers and Canadian census returns of 1901 and 1911, the age at death of each individual can be verified and the validated data used for our death rate calculations. These are then smoothed using a flexible P-spline approach. These data are complemented with data on septuagenarians, octogenarians, and nonagenarians in Quebec from the Canadian Human Mortality Database. Our results, limited to females due to low numbers of male survivors at very advanced ages, show that the rate of mortality increase slows down with age in this population, thereby supporting the occurrence of mortality deceleration at older ages among humans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".