Modeling probability of waterfowl encounters from satellite imagery of habitat in the central Canadian arctic
Bibliographic record
Abstract
Abstract We used aerial survey and corresponding digital land cover data to develop species‐habitat models to describe breeding‐ground distributions and landscape‐level habitat associations of greater white‐fronted geese (Anser albifrons), Canada (Branta canadensis) and cackling geese (B. hutchinsii), tundra swans (Cygnus columbianus), king eiders (Somateria spectabilis), and long‐tailed ducks (Clangula hyemalis). We then used habitat associations in the Queen Maud Gulf Migratory Bird Sanctuary and the Rasmussen Lowlands, Nunavut, Canada, in models to predict distributions of focal species in each study area. We used the receiver operating characteristic (ROC) method and the area‐under‐the‐curve (AUC) metric to evaluate predictive accuracy (hereafter, quality) of models. In the Queen Maud Gulf, AUC values suggested reasonable model discrimination for white‐fronted geese, Canada geese, and tundra swans (i.e., AUC > 0.7). Quality of species‐habitat models for king eiders and long‐tailed ducks was less than other species considered, but these models still predicted encounters and non‐encounters significantly better than the null model. For all species, quality of species‐habitat models was lesser for the Rasmussen Lowlands than for the Queen Maud Gulf, although discrimination ability for Rasmussen Lowland distributions remained significantly better than corresponding null models for geese and swans, but not for seaducks. Our research suggested that species' distributions modeled with landscape‐level habitat data is a tractable method to 1) identify habitat associations, 2) determine key habitats and regions, and 3) predict probable summer distributions of some waterfowl species over relatively large areas of the arctic from satellite imagery. © 2013 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".