Optimal life histories and food web position: linkages among somatic growth, reproductive investment, and mortality
Bibliographic record
Abstract
Life history variation among 60 Ontario populations of lake trout (Salvelinus namaycush), walleye (Sander vitreus), cisco (Coregonus artedii), and yellow perch (Perca flavescens) is presented and interpreted using a biphasic model of individual growth that specifically accounts for the significant shift in energy allocation that accompanies sexual maturity. We show that the constraints imposed on life history variation by the character of the biphasic growth model are such that optimal life histories will exhibit associations among growth parameters, reproductive investment, and mortality that are largely consistent with associations evident in both our data set and earlier empirical studies; the von Bertalanffy growth parameter k varies with reproductive investment, and both k and investment vary with adult mortality. Our analysis suggests that within a food web, life history parameters will shift in a predictable fashion with the decreases in mortality expected as one moves from primary consumers up toward top predators. This expectation is supported by the differences in life history parameters that we observe between the two top predators in our data set (lake trout and walleye) and the two mid-trophic level consumers (cisco and yellow perch).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".