DIETARY OVERLAP OF SYMPATRIC DIVING DUCKS DURING WINTER ON NORTHEASTERN LAKE ONTARIO
Bibliographic record
Abstract
Food resources available to diving ducks wintering on the Great Lakes have changed dramatically since the introduction of dreissenid mussels (Dreissena bugensis and D. polymorpha). We investigated the diets of Buffleheads (Bucephala albeola), Common Goldeneyes (B. clangula), and Long-tailed Ducks (Clangula hyemalis) during winter, 2002–2004, on northeastern Lake Ontario and determined the levels of dietary overlap. Dietary niche-breadth values were low, and dietary overlap values (prey size and type) were high for all species. Ducks primarily consumed high-quality, energy-dense prey (Amphipoda, Chironomidae), which were abundant. Our results highlighted three patterns: (1) dreissenid mussels constituted 85% of the macroinvertebrate community in Lake Ontario but were consumed in relatively low amounts during winter, (2) foods of high energy-density such as Amphipoda and Chironomidae were likely abundant enough for ducks to selectively feed on them, and (3) some constraint caused ducks to select energy-dense prey instead of the most available items (dreissenid mussels). Although the abundance of prey may have allowed numbers of diving ducks to increase in the past few decades on the Great Lakes, the long-term implications of high levels of dietary overlap among diving ducks is relatively unknown and warrants continued monitoring.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".