Factors influencing investigator-caused nest abandonment by North American dabbling ducks
Bibliographic record
Abstract
We examined factors (species, nest age, nest initiation date, clutch size, predator activity) that may affect the probability of investigator-caused nest abandonment in North American dabbling ducks and made predictions based on parental investment theory. For all nests, the best model contained species, nest stage, nest initiation date, and the interaction of species with nest initiation date. The probability of abandonment by Mallards ( Anas platyrhynchos L., 1758) was consistently higher than that of Blue-winged Teal ( Anas discors L., 1766). In these species, abandonment probability increased with later date, whereas Gadwall ( Anas strepera L., 1758), Northern Pintails ( Anas acuta L., 1758), and Northern Shovelers ( Anas clypeata L., 1758) showed the opposite pattern. Abandonment by all species declined as nest stage increased. Early-laying (≤5 eggs) females were 7 times more likely, and late-laying females were twice as likely, to abandon nests as incubating females. During incubation, abandonment probability was 38% higher during early (≤8 days) incubation than late incubation, and for each additional egg in a completed clutch, it was 19% lower. We propose a novel, two-stage model in which dabbling duck nest abandonment is influenced predominantly by opportunities for future reproduction during laying, and expected benefits from the current reproductive event during incubation.
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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 | 0.002 |
| 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 | 0.001 | 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".