Body condition of American Black Ducks (<i>Anas rubripes</i>) wintering in Atlantic Canada using carcass composition and a scaled mass index
Bibliographic record
Abstract
Body condition is commonly used in ecology to assess the physiological health of an organism or population and can be used to predict individual survival or breeding success. Waterfowl have been the focus of much research on body condition, and we studied body condition via carcass composition and using a scaled mass index (SMI) in American Black Ducks (Anas rubripes Brewster, 1902) wintering in coastal, agricultural, and urban areas of Atlantic Canada. Carcass composition varied between sexes and body mass decreased through winter as fat reserves depleted. Carcass composition was compared with American Black Ducks wintering in the United States, and American Black Ducks wintering in Atlantic Canada were structurally smaller yet proportionally fatter than those wintering in the United States, likely as a mechanism to survive Atlantic Canada’s harsher winters. SMI did not differ between coastal, agricultural, or urban American Black Ducks, indicating that despite known differences in the diets of the Black Ducks from these three areas, they can maintain similar body conditions capable of surviving the winter. We show that the SMI is a nondestructive alternative to study body condition in waterfowl. Our research highlights the adaptability and hardiness of American Black Ducks at the northern limit of their winter range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".