Environmental covariates for modelling the distribution and abundance of breeding ducks in northern North America: a review
Bibliographic record
Abstract
Many studies over the past 50 years have sought to identify environmental factors influencing breeding duck abundance and distribution in northern North America. Because results are currently scattered within the scientific literature, a summary of established duck-habitat associations would help to orientate future modelling research. Our goal was to review the published research testing for duck-habitat associations in northern North America. We reviewed 124 studies, summarizing their geographical coverage and species representation, and then analyzing the duck-habitat associations they tested. We identified 533 associations on 133 covariates falling into 16 environmental classes. Covariates of the ‘wetland’ classes were the most frequently associated with ducks; among these, ‘wetland area’ and ‘wetland density’ were the most common. ‘Climate’ covariates were the second most common associations, suggesting the potential for projecting the effects of climate change on ducks. The best-documented anthropogenic class was ‘agriculture’, for which associations with ducks were mostly negative. However, relatively few studies tested for associations with covariates for anthropogenic disturbances, which suggests that more research is needed to support forecasts of duck distribution under future human activity. This review and the accompanying database of duck-habitat associations will support future modelling studies by facilitating the selection of suitable habitat covariates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".