Nest scrape design and clutch heat loss in Pectoral Sandpipers (<i>Calidris melanotos</i>)
Bibliographic record
Abstract
Summary 1. The reasons why birds construct nest scrapes, and the extent to which scrape designs reflect functional optima, are poorly understood. Working on Pectoral Sandpipers (Calidris melanotos, Vieillot), we investigated whether scrapes function to insulate clutches and are efficiently designed to reduce heat loss rates. 2. Excavating a scrape and using lining material reduced the rate at which an object positioned within a scrape lost heat by 9% and 25%, respectively, suggesting that lined scrapes insulate clutches. 3. The rate of heat loss from an object within a scrape increased with scrape depth and decreased non‐linearly with lining depth. The extent to which wind increased forced convective heat loss decreased with scrape depth. 4. On average, Pectoral Sandpipers used the minimum lining depth that approximately minimized heat loss through the lining. Mean scrape depth approximately minimized convective cooling in windy conditions while minimizing heat loss to the ground. Pectoral Sandpiper scrapes therefore efficiently reduced heat loss given conflicting environmental thermal pressures. 5. Available lining materials differed in insulative quality when both damp and dry. Pectoral Sandpipers used lining materials that insulated relatively well when damp more than expected given random collection of locally available materials. Linings therefore insulated efficiently given the damp nesting environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".