Oldest-Old Mortality Rates and the Gompertz Law: A Theoretical and Empirical Study Based on Four Countries
Bibliographic record
Abstract
This is an interesting an important study of the oldest-old mortality in Canada, which includes construction of reliable life tables by the method of extinct generations, testing different statistical models to explain mortality age-trajectories, and analysis of temporal improvement in survival to advanced ages. In my opinion, this study has the following important strengths: (1) The study is based on survival data generated by the method of extinct generations. This is the best possible method to study survival at extreme ages, because traditional methods based on census data and age claims by nonagenarians and centenarians are extremely unreliable. Now, when a profound changes in the oldest-old mortality are observed in many developed countries, it is important to organize a continuous international monitoring of mortality trends at advanced ages (which is also important for regular correction of forecasts), and the method of extinct generations is the best possible way to address the problem of data quality. (2) The study uses several alternative models (mortality laws) that are applied to survival data and compared to each other. This is an important approach that needs to be developed
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".