Plant production after defoliation of native, Northern Mixed Prairie on hummocky terrain in Saskatchewan
Bibliographic record
Abstract
Resting plants after grazing is central to sustaining potential plant production. Growth of graminoids and forbs was determined for 3 yr after a single defoliation to 7.5 cm in May, June, July, August, September, October, November, or April on five different landform elements in the Northern Mixed Prairie. Green standing crop and cumulative green standing crop of forbs, graminoids, and their total varied with months of defoliation, landform elements, and years after defoliation. Green standing crop and cumulative green standing crop of forbs, graminoids, and their total was less than the control (P ≤ 0.05) on at least one of five landform elements until the second year after defoliation. This pattern of growth suggests the need to defer grazing for at least 1 yr after use to allow plants to regain their production potential. Plants defoliated early in the growing season recovered their production sooner or at the same time as those defoliated later in the growing season or when dormant. Generalizations that spring or early summer defoliation of native range reduces production and late summer or dormant season grazing has no effect on production warrants reconsideration where adequate rest is provided following a short period of grazing.Key words: Landform, landscape, primary production, rangeland, regrowth, standing crop
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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) | 0.001 | 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".