Ecological and Physiological Factors Affecting Brood Patch Area and Prolactin Levels in Arctic-Nesting Geese
Bibliographic record
Abstract
Abstract We investigated effects of ecological and physiological factors on brood patch area and prolactin levels in free-ranging Lesser Snow Geese (Chen caerulescens caerulescens; hereafter “Snow Geese”) and Ross's Geese (C. rossii). On the basis of the body-size hypothesis, we predicted that the relationships between prolactin levels, brood patch area, and body condition would be stronger in Ross's Geese than in the larger Snow Geese. We found that brood patch area was positively related to clutch volume and inversely related to prolactin levels in Ross's Geese, but not in Snow Geese. Nest size, nest habitat, and first egg date did not affect brood patch area in either species. Prolactin levels increased as incubation progressed in female Snow Geese, but this relationship was not significant in Ross's Geese. Prolactin levels and body condition (as indexed by size-adjusted body mass) were inversely related in Ross's Geese, but not in Snow Geese. Our findings are consistent with the prediction that relationships between prolactin levels, brood patch area, and body condition are relatively stronger in Ross's Geese, because they mobilize endogenous reserves at faster rates than Snow Geese. Factores Ecológicos y Fisiológicos que Afectan el Área del Parche de Incubación y los Niveles de Prolactina en Gansos Nidificantes del Ártico
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".