Title: Factors Associated With Dusky Canada Goose Nesting and Nest Success on Artificial Nest Islands of the Western Copper River Delta.
Bibliographic record
Abstract
The population of dusky Canada geese (Branta canadensis occidentalis; hereafter, dusky geese) nesting on the western Copper River Delta (CRD) in south-central Alaska has been in decline since the late 1970s. In an effort to alleviate mammalian predation, increase nest success, and avoid a listing under the U.S. Endangered Species Act, an artificial nest island (island) program was implemented on the western CRD in 1983. The installation of new islands on the CRD is the sole management action of the Pacific Flyway Council on the breeding grounds, but no comprehensive evaluation of the program has been published. I examined general trends in island use and nest success over time for three island types (donut islands, fiberglass floater islands, and sandbag islands) from 1984-2005. I used data from the island program to identify factors associated with dusky Canada goose nesting (hereafter, use) and nest success on islands from 1996-2005. I generated a series of candidate models and used logistic regression with model selection techniques to determine how variables representing pond characteristics, vegetative characteristics, interactions with conspecifics and larid species, the previous year’s island status, and the distance to predator corridors were associated with island use and nest success for each year. Use of islands by dusky geese nesting on the western CRD increased
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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".