Site-specific flight speeds of nonbreeding Pacific dunlins as a measure of the quality of a foraging habitat
Bibliographic record
Abstract
Many studies have investigated how foraging behavior such as prey choice varies with factors such as prey size or density. Models of such relationships can be applied “in reverse” to translate easily observed foraging behaviors into assays of habitat attributes that cannot (easily) be measured directly. One such model analyzes the speed of a forager flying between patches, where it captures prey. Faster flight shortens the travel time and hence elevates the intake rate, but is increasingly expensive. The model shows that the net intake rate is maximized at the point at which the energetic cost of flight is equivalent to the net rate of intake. Easy-to-measure flight speeds can thus be translated into hard-to-measure foraging intake rates using established flight power relationships. We studied nonbreeding Pacific dunlins ( Calidris alpina pacifica ) at 4 intertidal sites on the Fraser River estuary, British Columbia, Canada. These sites differed sufficiently that we expected food availability and hence the attainable foraging rate to differ. We measured interpatch flight speeds of dunlins foraging along the tideline within each site. The measured ground speed, calculated airspeed, and the statistically derived zero-wind effect airspeed all differed significantly between sites, matching in rank order our expectation of habitat quality based on their physical differences. Intake rate estimates ranged from 4.10W (best mudflat) to 3.48W (poorest). We think it unlikely that we would have been able to find such small differences using direct measures of foraging intake.
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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 | 0.001 | 0.001 |
| 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".