Photoperiod alters pain responsiveness via changes in pelage characteristics
Bibliographic record
Abstract
Small mammals use day length to adjust morphology and physiology to anticipate seasonal changes in environmental conditions. The canonical photoperiod-mediated annual adaptation is seasonal breeding. However, increasing evidence suggests that day-length information can induce plasticity in the nervous system, and thus provoke behavioral plasticity that can aid in winter survival. We hypothesized that low temperatures and reduced food availability in the winter would necessitate the evolution of increased pain tolerance mediated by short day lengths. Siberian hamsters ( Phodopus sungorus (Pallas, 1773)) housed in short days regressed their reproductive tracts and molted to winter pelage. Short-day hamsters also displayed elevated latencies of nociceptive responses in the hot-plate test, suggesting reduced pain responsivity. Prior to assessing potential neuronal or neuroendocrine mediators of altered pain responses, however, we investigated the possibility that changes in fur characteristics mediated photoperiod differences in pain responsivity. Removal of fur with a depilatory cream eliminated photoperiod differences in pain responsivity. Taken together, these data indicate that day length regulates thermal pain responses via changes in fur properties; also, changes in pelage properties have both thermoregulatory and thermal insulatory properties.
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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".