Life tables and graphs for Bahry (2022) - Equilibrium conditions in the evolution of senescence [MSc thesis, Carleton Univeristy]
Bibliographic record
Abstract
Life table data, and derived quantities, for Equilibrium Conditions in the Evolution of Senescence (Bahry, 2022, MSc thesis); adapted from the supplementary data of (Jones et al., 2014). Life table data for human (Japan 2009), human (Aché hunter-gatherer), fruit fly, Soay sheep, freshwater hydra, and desert tortoise. Basic life table quantities: age interval \((X)\); survival function \((l_X)\); and age-specific interval fecundity \((m_X)\). Derived quantities include interval average force of mortality; reproductive value; residual reproductive value; Hamilton's indicators of the age-specific forces of selection; and actual age-specific mortality vs. predicted age-specific mortality based on models treated in (Bahry, 2022). In the original life tables of Jones et al. (2014), desert tortoises negatively senesce over the range of observed ages, but had a final observed cut-off age of 74; this causes reproductive value to artifactually fall to 0 as age-approached the cutoff. To get around this, I also used an extrapolated desert tortoise life table, assuming the age-74 mortality and fecundity rates remained constant until age 1000, then using the extrapolated life table to calculate reproductive value (and Hamilton's indicators) up to the cutoff age 74. References Bahry, D. (2022). Equilibrium Conditions in the Evolution of Senescence [Master's thesis, Carleton University]. Jones, O. R. et al. (2014). Diversity of ageing across the tree of life. Nature 505: 169–174. https://doi.org/10.1038/nature12789
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.157 | 0.024 |
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".