Northern temperate pastures exhibit divergent plant community responses to management and disturbance legacies identified through a producer survey
Bibliographic record
Abstract
Abstract Questions Does plant composition differ among grasslands having divergent disturbance and management histories? Which vegetation attributes and disturbances specifically influence rangeland health? Location Northern temperate pastures in the Central Parkland and adjacent Boreal natural regions of central Alberta, Canada. Methods Plant composition and rangeland health data from 102 pastures were related to disturbance history acquired from retrospective producer surveys. Health assessments evaluated indicators such as vegetation composition, hydrologic function, and site stability. Producers were asked to identify pasture history (e.g., last cultivation date, whether fire had occurred), contemporary grazing actions (timing of use, grazing systems, type of livestock), and other management activities (i.e. herbicide application, manure spreading). Results Cultivation and burn history were the primary drivers of vegetation differences, where previously cultivated pastures were dominated by introduced, naturalized grasses, eliminating most native species. Remnant non‐cultivated grasslands were occupied by a mix of native and invasive plant species. Grazing system had limited impact on vegetation composition due to uniformly high livestock stocking. Plant composition corresponded with gradients in rangeland health, where the latter declined with increased stocking rate and supplemental feeding on pasture. Greater health was characterized by increased cover of graminoids (primarily introduced forages), abundant litter, low plant richness, and reduced stocking rates. Other land management activities had comparatively less impact on the composition and health of these grasslands. Conclusions Conversion of native grassland and forest in this region has a strong legacy effect on vegetation. Management actions associated with high stocking rates and supplemental feeding lowered rangeland health.
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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 | 0.000 | 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".