Bibliographic record
Abstract
This chapter considers how an individual's chance of dying is influenced by its age and sex. After a preliminary discussion about age-specific death rates, we will review the various ways of constructing life tables, which tabulate the information on age-specific death rates in an orderly way, and finally we will compare some of the life tables of different species of mammals and birds. Age-specific death rates We can develop our understanding of age-specific death rates by considering the work of Peter and Rosemary Grant on the large cactus (ground) finch ( Geospiza conirostris ), in the Galápagos archipelago. During the period 1978–83 they marked 1244 nestlings and followed their subsequent survival year by year. The nestlings could not be sexed, and they made the reasonable assumption that half were male and half were female. Only 27 of the 622 female nestlings survived for one year, 20 for two years, 13 for three years, and so on, until all the females were dead by seven years of age (see Table 14.1 for full data set). If we plot the number of survivors versus age (Fig. 14.1) we obtain the shape of the survivorship curve. The heavy early mortality obscures the shape of the curve beyond the first year of age. We can deal with this problem by plotting the number of survivors on a logarithmic scale (Fig. 14.2).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".