Bibliographic record
Abstract
Hereditary metabolic diseases in the context of evolutionary biology elicit interesting questions about ageing and senescence: Will persons successfully treated for inborn errors of metabolism, age and die prematurely because of compromised longevity? Because some unhealthy longevity has its origins in germline and somatic mutational processes, and in an inability to withstand metabolic stress, are there lessons to be learned about senescence from hereditary metabolic disease? Why are ageing, senescence and death necessary for Homo sapiens and how do they happen? These questions form the theme upon which several variations are played during the course of this essay. The theory of the disposable soma recognizes genomic and environmental events, well-seasoned by Chance, as determinants of ageing and senescence. Together, they cause the somatic damage that results in death. Genomics will reveal genes involved in longevity, both healthy and unhealthy. There will be schedules of gene expression behind our life-history traits. As in the field of hereditary metabolic disease, analogous genetic enquiries about ageing can be formulated. For example, how will heterozygotes age? Will association studies in centenarians reveal 'longevity genes'? Will disparate longevity in sib pairs reveal genetic factors? If there are 'ageing' mutations, of what types and with what effects? Will these initiatives lead to healthier longevity? A deeper question yet remains: why has human biology invested so greatly in grandparenthood?
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".