Waterfowl Use of Dense Nesting Cover in the Canadian Parklands
Bibliographic record
Abstract
ABSTRACT Dense nesting cover (DNC) has been a conspicuous component of habitat management for upland‐nesting ducks for >30 years, but its benefits for nesting ducks have been contentious. During 1994–1999 we monitored 3,058 dabbling duck ( Anas spp.) nests in 84 DNC fields located throughout the Canadian Parklands to examine sources of among‐field variation in nest density and nesting success. Nest density averaged 1.51 (SE=0.15) nests/ha and overall nesting success was 20.4%, but there was pronounced annual variation in both estimates. Nesting success increased with increasing field size (range = 6–111 ha), but nest density remained constant. Nest density increased with percent wetland habitat within DNC fields and declined with percent perennial cover in the surrounding 2.4 × 2.4‐km landscape, but these variables were not important for predicting nesting success. Nest abundance and nesting success roughly doubled in fields seeded with alfalfa ( Medicago sativa ) or sweet clovers ( Melilotus spp.), but there was no benefit from using native as opposed to tame grasses. We recommend that waterfowl managers in the Canadian Parklands establish DNC with alfalfa in large fields in landscapes with abundant wetlands but minimal competing cover.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".