A Spatial Analysis of Factors Affecting Nesting Success of Shorebirds in the Canadian Prairies
Bibliographic record
Abstract
Conversion of natural grassland to cropland has been postulated as a cause for population declines among grassland birds. We evaluated nest-site habitat characteristics and landscape composition for three species of prairie-breeding shorebirds nesting across the agricultural landscape of prairie Canada. Nests of the Upland Sandpiper (Bartramia longicauda) and Willet (Tringa semipalmata) were found disproportionately in natural grazed (57%) or natural idled (21%) patches, whereas Marbled Godwit (Limosa fedoa) nests were in habitats in proportion to their availability on the landscape. We examined multiple landscape variables (proportions of crop, natural idled, natural grazed, and wetland habitats, edge density, and patch area) across a broad geographic scale, using relatively large samples of nests, and found no strong relationships. Neither nest-site habitat characteristics nor landscape-level effects influenced daily survival rates of Upland Sandpiper or Willet nests. Variation in landscape composition at three different spatial scales (200, 600, and 2000 m) also did not influence daily nest-survival rates. To adequately inform land-use management programs aimed at shorebird conservation, further research should examine site-selection patterns and characteristics of nest sites that maximize survival of chicks and adults during subsequent stages in reproduction.
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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 | 0.000 | 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".