Effects of spring conditions on breeding propensity of Greater Snow Goose females
Bibliographic record
Abstract
Breeding propensity, defined as the probability that a sexually mature adult will breed in a given year, is an important determinant of annual productivity. It is also one of the least known demographic parameters in vertebrates. We studied the relationship between breeding propensity and conditions on spring staging areas (a spring conservation hunt) and the breeding grounds (spring snow cover) in Greater Snow Geese (Chen caerulescens atlantica), a long distance migrant that breeds in the High Arctic. We combined information from mark–recapture, telemetry, and nest survey data to estimate breeding propensity over a 7–year period. True temporal variation in breeding propensity was considerable (mean: 0.574 [95 % CI considering only process variation: 0.13 to 1.0]). Spring snow cover was negatively related to breeding propensity (bsnow=-2.05 ± 0.96 SE) and tended to be reduced in years with a spring hunt (b = -0.78 ± 0.35). Nest densities on the breeding colony and fall ratios of young:adults were good indices of annual variation in breeding propensity, with nest densities being slightly more precise. These results suggest that conditions encountered during the pre-breeding period can have a significant impact on productivity of Arctic-nesting birds
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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) | 0.001 | 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".