They're staying how long? Methods of and complications in determining stopover estimates using banding data
Bibliographic record
Abstract
We banded 4,034 nestlings in 1,433 successful Ferruginous Hawk (Buteo regafis) nests in Saskatchewan between 1969 and 2005.The unexplained but sudden and prolonged drop in ground squirrel numbers, 1987 -1996, had a less detrimental effect on Ferruginous Hawk productivity in grassland regions over ten consecutive years than was experienced by the Swainsoh's Hawk (Buteo swainsoni; Houston and Zazelenchuk 2004, Houston 2005).METHODS Since 1969, we have concentrated on banding Ferruginous Hawks on and near nine large Prairie Farm Rehabilitation Administration (PFRA) pastures in west-central Saskatchewan between RosetoWn and the Alberta boundary.These pastures host beef cattle and are without feed lots A map of the main banding area, with plots of percent natural grassland remaining, can be found in Schmutz et al. (2001).Our study area was not completely searched and had no well-defined boundaries.Over the years, we have increased search and banding efforts with the help of pasture managers and local resident birdwatchers.Records of ground squirrel numbers in western Canada are close to non-existent.Our visual, somewhat anecdotal, observations of ground squirrel abundance and their inverse relation to fox North American Bird Bander Vol. 30 No 4Ferruginous Hawk Productivity In Saskatchewan, 1969Saskatchewan, -2004
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".