Life-span and life expectancy - [Opinion]
Bibliographic record
Abstract
RAY KURZWEIL, GOOGLE'S CHIEF FUTURIST, says that if you can just hang on until 2029, medical advances will start to "add one additional year, every year, to your life expectancy. By that I don't mean life expectancy based on your birth date but rather your remaining life expectancy." Curious readers can calculate what this trend would do to the growth of the global population, but I will limit myself here to a brief review of survival realities. · In 1850, the combined life expectancies of men and women stood at around 40 years in the United States, Canada, Japan and much of Europe. Since then the values have followed an impressive, almost perfectly linear increase that nearly doubled them, to almost 80 years. Women live longer in all societies, with the current maximum at just above 87 years in Japan. · The trend may well continue for a few decades, given that life expectancies of elderly people in affluent countries rose almost linearly from 1950 to 2000 at a combined rate of about 34 days per year. But absent fundamental discoveries that change the way we age, this trend to longer life must weaken and finally end. The long-term trajectory of Japanese female life expectancies—from 81.91 years in 1990 to 87.26 years in 2017-fits a symmetrical logistic curve that is already close to its asymptote of about 90 years. The trajectories for other affluent countries also show the approaching ceiling. Records available show two distinct periods of rising longevity: Faster linear gains (about 20 years in half a century) prevailed until 1950, followed by slower gains. · If we are still far from the limit to the human life-span, then the largest survival gains should be recorded among the oldest people. This was indeed the case for studies conducted in France, Japan, the United States, and the United Kingdom from the 1970s to the early 1990s. Since then, however, the gains have leveled off.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.038 | 0.022 |
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".