Rapid estimation of plant biomass used as forage or cover by White-tailed Deer, Odocoileus virginianus, and Snowshoe Hare, Lepus americanus, in mixed and coniferous forests of southeastern Quebec
Bibliographic record
Abstract
Rapid estimation of plant biomass used as forage or cover by White-tailed Deer, Odocoileus virginianus, and Snowshoe Hare, Lepus americanus, in mixed and coniferous forests of southeastern Québec.Canadian Field-Naturalist 116(4): 523-528.Estimation of vegetation biomass in forest ecosystems is time-consuming because of inherent high variability.We developed 10 linear regression models to predict standing dry biomass of most plant taxa potentially used as forage or cover by White-tailed Deer and Snowshoe Hare in forests of southeastern Québec.Vertical interception and lateral coverage, which served as explanatory variables in regressions, were measured along 2-m transects before clipping, drying and weighing vegetation.Plant species with similar morphology and size were pooled.Although we collected vegetation samples from two areas 400 km apart, we could combine data for regressions except for low herbs (< 30 cm).Models covered a wide range of biomass and fitted the data reasonably well when In-transformed, with R? values varying between 0.61 and 0.85.Cross-validation confirmed model robustness.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".