Nest-site selection of a subarctic-breeding shorebird: evidence for tree avoidance without fitness consequences
Bibliographic record
Abstract
Vegetation communities in the subarctic are at risk of change due to climate-driven tree and shrub encroachment. Vegetation change may lead to unsuitable habitat for arctic-breeding birds, many of whom are declining. Although many possible factors are contributing to their decline, loss of breeding habitat could be a major contributor. We examined nest-site selection in Dunlin (Calidris alpina hudsonia (Todd, 1953)), a shorebird that nests in open fen habitats in the Churchill, Manitoba, Canada, region. Our objective was to determine whether this species avoids treed habitats and the possible fitness consequences for this. We examined the role of vegetative horizontal and vertical concealments on nest-site selection and nest fate. Dunlin selected nest sites with lower densities of trees than present at unused sites (40 m radius). Both horizontal and vertical concealments were significantly greater at nests than at unused sites, and horizontal concealment was greatest in the north. No measure of tree density or height, or concealment, significantly predicted nest fate. Although Dunlin appear to select nest sites that may minimize exposure to northerly winds and that may provide cover against potential predators, the current nest-site characteristics are not reinforced by contemporary 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 | 0.000 | 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".