Feeding Niche Overlap and Segregation Among Three GrouseSpecies Wintering in Hudson Bay Lowlands of Northern Ontario
Bibliographic record
Abstract
The Hudson Bay Lowlands of northern Ontario (comprising tundra and taiga) provide winter habitats for Willow Ptarmigan and Sharp-tailed Grouse as well as Rock Ptarmigan that occasionally fly in from arctic and subarctic regions. Winter food niche segregation/overlap was measured among these three species to investigate potential feeding competition; tamarack and berries were quantified by number and mass in the crops of grouse collected from the vicinity of the former community of Winisk, Ontario (now Peawanuck). All grouse species fed on birch and it was the principal food for Sharp-tailed Grouse. Willow was eaten primarily by Willow and Rock Ptarmigan. The proportions of ingested willow and birch parts differed greatly (P< 0.001) among the three grouse species but food niche overlap did exist among all three. Morisita's Index of food niche overlap, based on numbers of ingested items, was least (0.452/1.0) for Willow Ptarmigan and Sharp-tailed Grouse, intermediate (0.669/1.0) for Willow and Rock Ptarmigan, high (0.957./1.0) for Sharp-tailed Grouse and Rock Ptarmigan, and greatest (0.996/1.0) for Sharp-tailed Grouse at Peawanuck and Moosonee. Resident Willow Ptarmigan and Sharp-tailed Grouse may be segregated partially by habitat. Irruption of Rock Ptarmigan occurs irregularly across years, thus lowering feeding competition for birch parts with Sharp-tailed Grouse, and willow parts with Willow Ptarmigan.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".