Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.012 |
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 it