Potential lasting impacts of industrial herbicides on ungulate nutrition
Bibliographic record
Abstract
Glyphosate-based herbicides (GBH) are an effective, yet controversial, tool for reducing the survival and growth of deciduous vegetation in commercial tree plantations. While glyphosate residues can be stored in plants for over 10 years, the nutritional impacts of GBH for ungulate forage are unknown. Here we compare the energy and protein content of moose ( Alces alces) food plants in central British Columbia, Canada. Willow ( Salix bebbiana), dogwood ( Cornus sericea), and fireweed ( Chamaenerion angustifolium) samples were obtained from regenerating cut blocks 1, 3, 6, and 12 years after GBH treatment and from untreated controls with identical initiation dates. We predicted that surviving plants would exhibit reduced palatability, digestibility, and nutritional value compared to controls, and that these effects would last at least 1 year before dissipating. Contrary to predictions, concentrations of digestible protein were higher in treated blocks 1 year after herbicide application, but in subsequent years there were few significant differences in protein from treated versus untreated forage. Digestible energy concentrations were identical to controls 1 year after exposure, but significant reductions were observed after 12 years. Results indicate potential nonlinear, complex, and long-lasting effects of GBH on the constituents of understory plants with implications for ungulate forage quality.
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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 | 0.001 | 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".