Bibliographic record
Abstract
One of the most persistent misconceptions surrounding the prospect of combating aging – so persistent, in fact, that it has acquired a name, “the Tithonus error” – is that successful anti-aging interventions would postpone death but would not postpone the decline in health and vigor that characterizes later life. The psychological reasons for why so many people have for so long remained deaf to gerontologists' incessant and vocal correction of this error are complex and have been addressed in my previous work. Here I discuss the physiological basis for the confidence, shared by all biologists of aging, that the only way we will ever substantially extend the human lifespan is by extending people's healthy lifespan, rather than by keeping people alive in a frail state. I then discuss what these physiological realities tell us about which approaches to combating aging are the most promising, and why they are likely to lead to the substantial (and, eventually, dramatic) postponement of what is now humanity's number one killer. Introduction: the Tithonus error and the pro-aging trance Ill health is risky That is really the beginning and end of what I need to communicate in this section. It certainly does not seem particularly controversial. But, in practice, a phenomenal amount of effort has been expended in both asserting and resisting this simple truth.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".