Bibliographic record
Abstract
Humans have long been fascinated with mortality. We are simultaneously mesmerized by and fearful of aging, with abundant popular culture around the idea of eternal youth and a myriad of antiaging products claiming to make us look or feel younger. Death is an inevitability of life, but planet Earth's diverse creatures have a fascinating variety of life spans. Mayflies live just one day while Antarctic glass sponges may live for 15,000 years. Understanding why longevity varies so greatly across the diversity of life forms is one of the most compelling mysteries of science. Even within populations of the same species, individuals can have extraordinarily different life spans. In the fields of ecology and evolutionary biology, aging research is breaking new ground, while biogerontology—the study of the biological processes of aging—is at work to extend health in older years. ... Why we age and then die is a paradox scientists and philosophers have grappled with since Aristotle. If natural selection acts to optimize fitness, why does evolution not prevent age-related decline? In 1891, German biologist August Weismann, piggybacking on ideas from ancient Roman predecessors such as the philosopher-poet Lucretius, suggested that selection for aging provides a weeding out of older individuals to provide room for more fecund youngsters.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.206 | 0.087 |
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".