Mallard brood movements and wetland selection in the Canadian prairie parklands
Bibliographic record
Abstract
Nest success is the most important determinant of population growth in prairie waterfowl, and tremendous resources have been allocated to increasing nest success as part of the North American Waterfowl Management Plan. Although brood survival is also important to mallard (Anas platyrhynchos) recruitment rates, knowledge of brood ecology is severely lagging relative to nest success. My study addresses this information gap by exploring mallard brood movements and wetland selection. An increased understanding of mallard brood behaviour will allow landscape management decisions to be more considerate of brood needs, and should lead to greater recruitment. Data were collected in conjunction with the Prairie Habitat Joint Venture (PHJV) Assessment project from 15 65-km2 study areas located throughout the Canadian prairie parklands. A total of 308 mallard broods were radio-tracked from hatch until 3O-days post-hatch. Models were constructed to predict movement probability (repeated-measures logistic regression) and movement distance (ANCOVA) of broods in relation to brood age, date, and study area. A backwards-elimination procedure was used to simplify models by eliminating non-significant (P > 0.05) effects. Models also were constructed to predict wetland selection in relation to wetland permanence, cover type, width of flooded emergent vegetation, brood age, date, dominant vegetation, and percent of seasonal wetlands inundated with water. Information-theoretic techniques were used to select the best fitting models. Movement probability generally decreased with age, although results varied by hatch date and study area. Later hatched broods moved farther than early hatched broods. Permanence, cover type, the width of flooded emergent vegetation, and the dominant species of vegetation were all important predictors of wetland selection...
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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 | 0.001 | 0.001 |
| 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".