Bibliographic record
Abstract
Humans have sought to cheat death for as long as we have been cognizant of our mortality. History’s early explorers of the frontier of immortality include alchemists in the pursuit of an elixir of life and emperors who, ironically, hastened their own death from the consumption of mercurial concoctions. Scientifically grounded approaches to the extension of the human lifespan emerged in the 20th century and were based on hormonal rejuvenation, calorie restriction, and most recently, the consumption of supplements with purported anti-aging effects. A combination of three “longevity drugs” has recently been championed by Dr. David Sinclair, co-discoverer of the lifespan-regulating sirtuin enzymes, and author of the epigenetics-focused Information Theory of Aging (ITA). In this work, we investigate the evidence behind Sinclair’s ITA, highlight concerns related to his regimen, and reflect on the possibility that we are at a nexus in time preceding a dramatic increase in human healthspans. Promisingly, if the ITA holds true, individuals will be uniquely empowered to “hack” their own immortality.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".