Supercentenarians and Semi-supercentenarians in France
Bibliographic record
Abstract
Abstract The purpose of this study is twofold. Firstly, it attempts to exhaustively identify cases of French supercentenarians and semi-supercentenarians and to validate their alleged age at death. Secondly, it seeks to uncover careful patterns and trends in probabilities of death and life expectancy at very old ages in France. We use three sets of data with varying degrees of accuracy and coverage: nominative transcripts from the RNIPP ( Répertoire national d’identification des personnes physiques ), death records from the vital statistics system, and “public” lists of individual supercentenarians. The RNIPP stands out as the most reliable source. Based on all deaths registered in the RNIPP at the alleged ages of 110+ for extinct cohorts born between 1883 and 1901, errors are only few, at least for individuals who were born and died in France. For alleged semi-supercentenarians, age validation on a very large sample shows that errors are extremely rare, suggesting the RNIPP data can be used without any verification until age 108 at the minimum. Moreover, a comparison with “public” lists of individual supercentenarians reveals a single missing occurrence only in the RNIPP transcripts since 1991. While the quality of vital statistics data remains quite deficient at very old ages compared to RNIPP, the analytical results show a significant improvement over time at younger old ages. Our RNIPP-based probabilities of death for females appear to level-off at 0.5 between ages 108 and 111, but data becomes too scarce afterwards to assess the trend. Also, we obtain a quite low life expectancy value of 1.2 years at age 108.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".