Factors Associated with Dusky Canada Goose (<i>Branta Canadensis Occidentalis</i>) Nesting and Nest Success on Artificial Nest Islands of the Western Copper River Delta
Bibliographic record
Abstract
Decline of the Dusky Canada Goose (Branta canadensis occidentalis; hereafter, Dusky Goose) population on the western Copper River Delta (CRD) prompted the establishment of an artificial nest island (island) program in 1983. A retrospective analysis of the program was conducted to examine general trends in island use and nest success from 1984–2005. A series of candidate models was generated to determine how habitat, island and biological variables were associated with island use and nest success from 1996–2005. Use of islands by Dusky Geese increased between 1987 and 2005 from 10% to 44%; apparent nest success averaged 64 ± 4% and showed no trend with year. Island use was consistently and strongly associated with the previous year's island status. The odds of nesting on an island that contained a successful nest the previous year were four times greater than for islands not used the previous year. Likelihood of island use was highest at moderate shrub cover and increased with shrub height. Likelihood of nest success increased on islands further from shore. The influence of year suggests the presence of alternate prey and predator abundance is more important to nest success than island features. The increasing use of islands while the CRD Dusky Goose population has been declining indicates that islands may be increasingly important to population productivity. However, quantifying the contribution of the island program requires a better understanding of other population metrics, such as gosling mortality.
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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".