Abundance and Habitat Selection of Breeding Scoters (<em>Melanitta</em> spp.) in Ontario’s Hudson Bay Lowlands
Bibliographic record
Abstract
Concern about declining populations of sea ducks counted on the wintering grounds prompted a survey of sea ducks on the breeding grounds in the Hudson Bay Lowlands of Ontario in spring 2009. We estimated densities of breeding scoters (Surf Scoter, Melanitta perspicillata, White-winged Scoter, M. fusca, and Black Scoter, M. americana) and found the average estimates of Surf Scoters (average = 0.11 indicated pairs/km2) and Black Scoters (average = 0.16 indicated pairs/km2) to be as high as some of the highest reported for North America. We also conducted a habitat association analysis using resource selection functions (RSF) for indicated pairs of all scoter species combined at a scale of 250 m. Breeding pairs of scoters in the Hudson Bay Lowlands appear to have an affinity for smaller wetlands (≤100 ha) disproportionate to what is available, also avoiding lakes (i.e., wetlands >100 ha). Pairs were also found in areas with less forest cover and fen area than was available. An estimate of the area under the curve of the receiver operating characteristic suggests that these habitat association models have some utility. Once tested and validated with surveys beyond the current study area, these models can be refined and used to predict habitat use by breeding pairs of scoters in the Hudson Bay Lowlands; this information will be particularly useful for population estimation and land use planning.
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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 | 0.001 | 0.001 |
| 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".