Spatial variation in shorebird nest success: Implications for inference
Bibliographic record
Abstract
Estimates of nest success are widely applied in order to evaluate a multitude of theoretical and practical issues.Frequently, however, researchers fail to limit their inferences to the appropriate spatial scale.We evaluated small-scale variation in nest success of Western Sandpipers Calidris mauri during a four-year study on the Yukon-KuskokwimDelta in western Alaska.We use these data to demonstrate that small-scale variation in nest success can significantly alter a researcher's interpretation of the factors affecting that reproductive parameter.In the absence of a statistically valid sampling design, researchers must be very careful about making inferences for areas beyond their actual study site.Properly designed studies allow for broader inferential power, but the logistical and financial hurdles involved in designing and implementing such a study are daunting.Metareplication can enhance one's confidence in the interpretation of local results, but should not be seen as a substitute for well-designed sampling schemes implemented across broad geographic scales. INTRODUCTIONStudies of nesting success across a broad spectrum of avian taxa have multiplied dramatically over the last decade.Estimates of nest success have been used to evaluate a wide range of theoretical and practical issues, including the effects of habitat fragmentation, brood parasitism, and predation on nest success (e.g.,
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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 | 0.099 | 0.459 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".