Spatiotemporal Characterization of Non-breeding Great Shearwaters <i>Ardenna gravis</i> within their Wintering Range
Bibliographic record
Abstract
Movements of Great Shearwaters Ardenna gravis wintering in the Northwest Atlantic showed age-based spatial and temporal flexibility, with foraging tactics linked to a defined physical preference of their primary prey. From 2013 to 2018, we tracked 58 Great Shearwaters during their wintering season using platform terminal transmitters deployed in the same area of the southwest Gulf of Maine. Utilization distributions (UDs) for individual birds were created from convex hulls, which were then combined for spatial and temporal analyses. Of the 95% kernel UDs, 55% were contained within the Gulf of Maine and the remainder extended to the Scotian Shelf off Nova Scotia and the Grand Banks off Newfoundland. Analysis of fecal DNA from tagged birds and others captured with them indicated that Northern sand lance Ammodytes dubius were the primary prey while in the Gulf of Maine. This relationship was supported by the overlap of UDs and sand lance habitat. The spatial occurrence of sand lance from bottomfish trawl survey data demonstrated that these fish preferred shallow water (< 100 m deep) with substrates consisting of high sand content (> 50%) and grain sizes ranging from 0.35–2.00 mm in diameter. These same properties were associated and spatially aligned with the collective 25% kernel UD of Great Shearwaters. Necropsy of bycaught Great Shearwaters from an area that overlapped in space and time with tagged individuals and sand lance habitat demonstrated that most birds (89%) were young (0–2 years), based on gonadal development, molt score, and/or bursa of Fabricius. Coupling demographic information from necropsies with spatial habits and movement timing of tagged birds suggests this region serves as a winter “nursery” for Great Shearwaters.
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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 | 0.001 |
| 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".