Evaluating the grazing response index of three key forage species in the southern interior of British Columbia by using multiple clipping treatments to determine impact on plant vigour
Bibliographic record
Abstract
In British Columbia, many practices related to rangeland management are not working as effectively as they used to due to fluctuating environmental factors. The Grazing Response Index (GRI) is a tool which was developed in Colorado, USA to help rangeland managers and producers evaluate the effects of grazing in a current year by integrating management and climate factors which relate directly to growing conditions. To determine if this tool could be used in the Southern Interior of British Columbia, in conjunction with range condition assessments and range health assessments, the effectiveness of the GRI was determined by comparing the responses of three key forage species to various levels of clipping: clipped once at 40% or 70% or clipped three times at 40% or 70% removal of biomass. Results varied by species: bluebunch wheatgrass was impacted greater by intensity of clipping rather than frequency, rough fescue showed interactions between frequency and intensity while pinegrass results were variable. Though results varied by species, the GRI scoring for each species response was considered appropriate though it was conservative in its scoring of the more severe treatments. I conclude that the GRI could be a beneficial tool for annual range management in the Southern Interior to supplement long term management tools.
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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 | 0.000 |
| 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".