Temperature effects on the reproductive performance of fish
Bibliographic record
Abstract
Introduction Scientists are increasingly being called upon to predict the outcomes of global temperature changes on fish populations. Both reproduction and early development in fish are particularly sensitive to temperature perturbations. Numerous in vitro and in vivo studies have shown that reproductive endocrine homeostasis in fish (Fig. 1) is responsive to changes in temperature. This includes alterations in the secretion and actions of hormones associated with all components of the hypothalamic–pituitary–gonadal axis which controls reproductive processes. By comparison, far fewer studies have considered the longer term consequences of altered temperature profiles on the reproductive cycle or larval development. Even fewer studies have considered the long-term ecological consequences of multigenerational exposures to elevated thermal regimes. Given the existing data and the need for a rapid response to questions on the outcomes of temperature change, our best informed judgement will have to be based, in large part, on information from short-term assays. This leads to the question of whether current methods of assessing the effects of temperature on the molecular and cellular events mediating reproductive processes can be used to predict effects at whole animal and population levels (Fig. 2). This chapter reviews our current understanding of the effects of elevated temperature on reproductive performance in fish. The initial focus is on endocrine homeostasis and the effect of temperature on hormone biosynthesis, metabolism and actions.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".