Feeding Ecology of Greater Snow Goose Goslings in Mesic Tundra on Bylot Island, Nunavut, Canada
Bibliographic record
Abstract
Abstract Although mesic tundra is a habitat commonly used by arctic-nesting geese, their feeding ecology in this habitat is little known compared to wetlands. Our objectives were to determine the diet and food selection of Greater Snow Goose (Chen caerulescens atlantica) goslings in relation to the nutritional quality of plants growing in mesic tundra habitats on Bylot Island, Nunavut, Canada. We used two different but complementary approaches: examination of esophageal contents of sacrificed wild goslings, and direct observation of the feeding activity of captive, human-imprinted goslings. The latter method was innovative and provided a reliable description of the diet, with results comparable to those obtained from wild goslings. Although mesic habitats have a more diverse floristic composition than wetlands and sparse graminoid cover, Gramineae were preferentially selected and dominated the diet (~50%). The rest of the diet consisted mainly of members of the Juncaceae, Polygonaceae, and Leguminosae families. The diet of very young goslings was diverse, but as they aged and gained efficiency, they concentrated on a few taxa. Goslings ate mostly leaves (~80%), but flowers (~20%) were also important. Food selection was influenced by nitrogen and total phenolic compounds content of plants, but the ratio of phenolic compounds to nitrogen in plant organs was most determinative of food choice. Neutral detergent fiber content of plants did not influence plant selection. Both plant nutritional quality and availability determined gosling diet across different mesic habitats and growing goslings appeared to maximize their intake of metabolizable proteins.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".