Comparing individual and population measures of senescence across 10 years in a wild insect population
Bibliographic record
Abstract
Declines in survival and performance with advancing age (senescence) have been widely documented in natural populations, but whether patterns of senescence across traits reflect a common underlying process of biological ageing remains unclear. Senescence is typically characterized via assessments of the rate of change in mortality with age (actuarial senescence) or the rate of change in phenotypic performance with age (phenotypic senescence). Although both phenomena are considered indicative of underlying declines in somatic integrity, whether actuarial and phenotypic senescence rates are actually correlated has yet to be established. Here we present evidence of both actuarial and phenotypic senescence from a decade-long longitudinal field study of wild insects. By tagging every individual and using continuous video monitoring with a network of up to 140 video cameras, we were able to record survival and behavioral data on an entire adult population of field crickets. This reveals that both actuarial and phenotypic senescence vary substantially across 10 annual generations. This variation allows us to identify a strong correlation between actuarial and phenotypic measures of senescence. Our study demonstrates age-related phenotypic declines reflected in population level mortality rates and reveals that observations of senescence in a single year may not be representative of a general pattern.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".