Testing Williams’ prediction: reproductive effort versus residual reproductive value (RRV)
Bibliographic record
Abstract
Williams (1966; Am. Nat. 100(916): 687–690) furthered R.A. Fisher’s concept of reproductive value by breaking it into two components: (1) current reproduction and (2) residual reproductive value (RRV, the summed product of survival and reproduction over the rest of the lifespan). He predicted a negative correlation of measures of these two components among species, and this prediction led in part to the idea of trade-offs in life-history theory. We tested Williams’ prediction with 24 species of albatrosses and petrels (order Procellariiformes), species with a great range of body sizes and all laying only one egg at a time (like humans, highly iteroparous). Two measures of reproductive investment were not negatively correlated with RRV. Adjusting data for body mass and phylogeny resulted in significant positive associations. In addition, any measure of annual parental allocation to reproduction (once adjusted for body size) should give a positive association with RRV as shown by a simple simulation model that assumes a highly iteroparous life cycle. Under such life cycles, Williams’ prediction confounds the positive influence of reproduction on both current investment and RRV. Principles of life-history theory, however, do not require re-evaluation, as this particular prediction can in at least some cases be internally inconsistent.
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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 | 0.016 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".