Predicting waterbird nest distributions on the Yukon–Kuskokwim Delta of Alaska
Bibliographic record
Abstract
ABSTRACT The Yukon–Kuskokwim Delta of Alaska, USA is a globally important region for numerous avian species including millions of migrating and nesting waterbirds. However, data on the current spatial distribution of critical nesting areas and the importance of environmental variables in the selection of nest locations are generally lacking for waterbirds in this region. We modeled nest densities for 6 species of geese and eiders that commonly breed on the Yukon–Kuskokwim Delta, including cackling goose (Branta hutchinsii minima), emperor goose (Chen canagica), black brant (B. bernicla nigricans), greater white‐fronted goose (Anser albifrons frontalis), spectacled eider (Somateria fischeri), and common eider (S. mollissima). The data used were from single‐visit nest searches on 2,318 plots sampled during 29 years from 1985 to 2013. We modeled nest density for each species by combining data across years and using random forests methods and time‐static landscape environmental variables. These models provide the first habitat‐specific predictive distributions of nest density for these species breeding on the Yukon–Kuskokwim Delta of Alaska. Predictive performance of the random forests models varied among species, explaining 13–69% of the variance in nest density. For most species, nest density was greatest near the coast and within lowland habitats. Predicted nest densities mapped across the coastal zone of the Yukon–Kuskokwim Delta revealed areas of high and low nest densities that can be used to inform management and conservation decisions. © 2017 The Wildlife Society.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".