Environment-specific and sex-specific allocation strategies among gonadal, somatic, and immune indices in a marine fish
Bibliographic record
Abstract
We examined the relative investment in somatic, gonadal, and liver growth, as well as a measure of immune function, in a marine species of fish (cabezon, Scorpaenichthys marmoratus (Ayres, 1854)) reared under different ambient temperature regimes (mean temperatures: 14.8 °C in Santa Barbara and 12.7 °C in Cayucos; both in California, USA). We predicted that fish reared in colder water would exhibit more pronounced trade-offs among growth, development, and immune parameters than fish in warmer water, and that females would have more pronounced trade-offs than males due to increased energy requirements for ovary development. We found immune function and liver investment were positively related in cold-water fish, but unrelated in warmer water fish. Immune function positively covaried with gonadal investment, but was not associated with somatic investment. Gonadal investment was negatively related to somatic investment, but this was driven by females, as there was no relationship between gonad development and somatic growth in males. We also found that the sexes differed in the relationship between gonadal and liver investments, in which females again exhibited a negative association but males exhibited a positive association. These results indicate that developmental investment strategies in cabezon are flexible and may be both context-specific and sex-specific.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".