Multi‐level habitat selection of boreal breeding mallards
Bibliographic record
Abstract
Abstract Canada's western boreal forest is a vital breeding habitat for North American duck populations. This region has experienced considerable demand for its valuable natural resources (e.g., oil and gas, forestry) resulting in extensive industrial development (e.g., infrastructure), which is predicted to continue. The potential effects of industrial development on breeding ducks in the western boreal forest, however, remains largely unexplored. We used backpack harness global positioning system (GPS) transmitters to document habitat selection by breeding female mallards (Anas platyrhynchos) across a gradient of industrial development in the western boreal forest of Alberta, Canada. We modeled breeding home range (second order) selection and habitat selection within the home range (third order) using resource selection functions, and spatially predicted our models across the landscape to highlight important breeding habitat. Contrary to our predictions, breeding female mallards did not avoid all industrial development at the second and third orders. Females established home ranges (second order) with greater proportions of marsh, graminoid fen, and well pads, and lower proportions of forest. Within their home range (third order), females selected shrub swamp, graminoid fen, marsh, well pads, and borrow pits, and avoided open water, swamp, treed peatland, forest, forest harvest areas, and industrials (e.g., buildings). Females also selected habitat that was close to pipelines and roads. Our results suggest that the magnitude and direction of breeding season habitat selection by female mallards varies depending on the scale and landscape features, but current levels of industrial development within our study area still allowed for the establishment of breeding season home ranges. Our spatially predicted maps contribute to the increasing body of work surrounding boreal waterfowl ecology by highlighting important breeding habitat for mallards in Canada's western boreal forest.
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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 | 0.000 |
| 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".