Information theory and the neuropeptidergic regulation of seasonal reproduction in mammals and birds
Bibliographic record
Abstract
Seasonal breeding in the temperate zone is a dramatic example of a naturally occurring change in physiology and behaviour. Cues that predict periods of environmental amelioration favourable for breeding must be processed by the brain so that the appropriate responses in reproductive physiology can be implemented. The neural integration of several environmental cues converges on discrete hypothalamic neurons in order to regulate reproductive physiology. Gonadotrophin-releasing hormone-1 (GnRH1) and Kisspeptin (Kiss1) neurons in avian and mammalian species, respectively, show marked variation in expression that is positively associated with breeding state. We applied the constancy/contingency model of predictability to investigate how GnRH1 and Kiss1 integrate different environmental cues to regulate reproduction. We show that variation in GnRH1 from a highly seasonal avian species exhibits a predictive change that is primarily based on contingency information. Opportunistic species have low measures of predictability and exhibit a greater contribution of constancy information that is sex-dependent. In hamsters, Kiss1 exhibited a predictive change in expression that was predominantly contingency information and is anatomically localized. The model applied here provides a framework for studies geared towards determining the impact of variation in climate patterns to reproductive success in vertebrate species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".