Seasonal variation in plant nutritive quality for Greater Snow Goose goslings in mesic tundra
Bibliographic record
Abstract
Variation in nutritive quality over time and among forage plants is important for herbivores such as geese. We examined the seasonal variation of some nutritive attributes (nitrogen, neutral detergent fibre, and phenolic compounds) of five plant species consumed by Greater Snow Geese ( Chen caerulescens atlantica ) L. in mesic tundra, a habitat where goose feeding ecology has been little studied compared with wetlands. We sampled ungrazed, aboveground plant tissues five times at 10–14 d intervals between 1 July and 15 August 2003 on Bylot Island, Nunavut. The species were Arctagrostis latifolia (R. Br.) Griseb. (Gramineae), Luzula nivalis (Laest.) Beurl. (Juncaceae), Oxytropis maydelliana Trautv. (Leguminosae), Oxyria digyna (L.) Hill, and Polygonum viviparum L. (both Polygonaceae). All species showed a seasonal decline in nitrogen content in both leaves and flowering heads (includes flowers and fruits) but the amplitude was variable among species (from 10% to 62% decline depending on the species). Neutral detergent fibre concentration in leaves remained stable or increased slightly over time in contrast to flowering heads where it increased in all species (from 7% to 94%). Fibre content was higher in flowering heads than in leaves. The total content of phenolic compounds varied throughout the summer. In some cases, the content of phenolic compounds remained stable but in others it initially increased and then decreased later on, or it increased throughout the summer. Seasonal variations in plant nutritive quality were smaller than interspecific differences. The nitrogen content of forbs (especially Oxytropis ) was high and their fibre content low compared with the grass and rush species (Luzula), particularly during the early summer.
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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 | 0.000 | 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) | 0.000 | 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".