Kane Tanaka’s 119 birthday and the Supercentenarians’ age estimation. Further remarks on the oldest old record of 122 years by Jeanne Calment
Bibliographic record
Abstract
In a previous study, based on the large number of centenarians in Japan, we had constructed a model to estimate the supercentenarians in the country, https://doi.org/10.1007/978-3-319-76002-5_2 . The model was published in Volume 46 of The Springer Series on Demographic Methods and Population Analysis. Projections are done and forecasts are made for the maximum expected age at death of record holder Kane Tanaka, now at 119 years old. Now, February 22, 2022, we have used the Saito-Ishii-Robine (2021) death data set for females (100 to 118 years old) in Japan from 1951 to 2015 to test an advanced model. We fit this model to data from 100 to 109 years of age and make projections from 110 to 119 years of age. The fit and projections apply perfectly. We have also used data from the Human Mortality Database (HMD) for female deaths (100 to +110 years old) in Japan from 1950 to 2019. The fit and projections verify the expected one supercentenarian at 119 years of age. The same model was applied to centenarian female deaths from 1950 to 2019 in a large number of countries (Europe-USA-Canada-Australia-New Zealand and Japan). By this method, a large amount of death data is selected, adequate to find extreme age supercentenarians. The fit and projections led to at least one supercentenarian at 122 years of age; that is Jeanne Calment’s record. Having estimated the two parameters of the model (slope and curvature) for these countries, we have fitted the Gerontology Research Group (GRG) database set, which led to an expectation of a supercentenarian at 120 years of age and a good probability for a 121-year-old supercentenarian in the world. A recent publication by Lenart A., Aburto J.M., Stockmarr A., Vaupel J.W. (2021) confirms our findings from 2018 regarding Calment’s record. Our provided formula estimates one supercentenarian at age 122 in the year 2045 (Skiadas, 2018) to reach the Calment’s record.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".