Do ducks and songbirds initiate more nests when the probability of survival is greater?
Bibliographic record
Abstract
Nesting chronology in grassland birds can vary by species, locality, and year. The date a nest is initiated can influence the subsequent probability of its survival in some grassland bird species. Because predation is the most significant cause of nest loss in grassland birds, we examined the relation between timing of nesting and nest survival. Periods of high nest survival that correspond with the peak of nesting activity might reflect long-term adaptations to specific predation pressures commonly recurring during certain periods of the nesting cycle. We evaluated this theory by comparing timing of nesting with date-specific nest survival rates for several duck and passerine species breeding in north-central North Dakota during 1998–2003. Nest survival decreased seasonally with date for five of the seven species we studied. We found little evidence to support consistent relations between timing of nesting, the number of nest initiations, and nest survival for any species we studied, suggesting that factors other than nest predation may better explain nesting chronology for these species. The apparent mismatch between date-specific patterns of nest survival and nest initiation underscores uncertainty about the process of avian nest site selection driven mainly by predation. Although timing of nesting differed among species, the general nesting period was fairly predictable across all years of study, suggesting the potential for research activities or management actions to be timed to take advantage of known periods when nests are active (or inactive). However, our results do not support the notion that biologists can take advantage of periods when many nests are active and survival is also high.
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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 | 0.001 |
| 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".