Winter irruptive Snowy Owls (<i>Bubo scandiacus</i>) in North America are not starving
Bibliographic record
Abstract
Winter irruptions, defined as irregular massive movement of individuals over large distances, have been linked to food supply. Two hypotheses have been put forward: the “lack-of-food” suggests that a shortage of food forces individuals to leave their regular winter range and the “breeding output” suggests that unusually large food supplies during the preceding breeding season allows production of a large number of offspring dispersing in winter. According to the breeding output hypothesis, irruptive Snowy Owls (Bubo scandiacus (Linnaeus, 1758)) in eastern North America should not exhibit a lower body condition than individuals in regular wintering regions and individuals on the breeding grounds. Additionally, body condition of irruptive individuals should be unrelated to irruption intensity. Although body condition of juveniles was generally lower than that of adults and improved during the winter, we measured a fair body condition in both juvenile and adult irruptive Snowy Owls across North America. The results showed that Snowy Owls are not in a starving state during winter and that body condition of all age classes was not related to winter irruption intensity. Those results support the breeding output hypothesis suggesting that winter irruptions seem to be primarily the result of a large number of offspring produced when food availability on the breeding grounds is high.
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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 | 0.001 | 0.001 |
| 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".