Indicators of avian nest predation and parental activity in a managed boreal forest: an assessment at two spatial scales
Bibliographic record
Abstract
Studies of avian nesting success at the landscape level often use a single indirect measure to evaluate nest predation or parental activity. During two summers at Forêt Montmorency, Quebec, we analyzed and compared three indirect measures of nest predation risk: detection of two nest predators, (1) red squirrel (Tamiasciurus hudsonicus Erxleben) and (2) gray jay (Perisoreus canadensis L.), (3) depredation of bait, and direct observations of parental activity (mostly food transported by adults) at 316 stations over a 230-km2area. We assessed the relationship between these indicators and three landscape-structure variables (total forested area, total core area, and total edge length) at two spatial scales (83 and 1610 ha). Nest predators were generally present at <30% of stations, <20% of baits were depredated, and >40% of stations exhibited evidence of broods. Bait depredation and the detection of jays or squirrels were correlated, but we found no associations between nest predation and parental activity indicators. Indicators of nest predation and parental activity were not significantly heterogeneous over the study area, despite substantial variation of landscape structure. We argue that parental activity indicators may be more reliable than nest predation indicators, but only as a coarse way to detect variation in nesting success.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".