Nest-site selection in the barn swallow, <i>Hirundo rustica</i>: What predicts seasonal reproductive success?
Bibliographic record
Abstract
Despite hundreds of studies, we know very little about the causes and fitness-related consequences of nest-site selection. For example, it is not typically known whether the rarely reported fitness consequences of site selection are the result of nest, individual, or nest-site variables or combinations of these factors. Reuse of previously constructed nest sites is a prevalent behavior in many animals and offers the opportunity to experimentally tease apart whether seasonal reproductive success is a function of nest, individual, or nest-site characteristics. I used observational and experimental data to test three hypotheses related to these factors in association with barn swallow ( Hirundo rustica L., 1758) nest reuse. While both nest and individual characteristics explain variation in seasonal reproductive success, nest location per se is not an important factor defining the outcome of nest-site selection. Whereas traits related to habitat and individual characteristics are likely confounded in correlational studies, my experiments demonstrate a causal relationship between seasonal reproductive success and aspects of the nest and individual, the latter explaining more variation in the model than nest characteristics. Knowledge of the relative roles of individual, nest, and nest-site attributes are important for understanding the causes and consequences of habitat selection behavior.
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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".