Linking management practices with range health in northern temperate pastures
Bibliographic record
Abstract
Little information exists on the management and range health (RH) of northern temperate pastures, where health is defined as the ability to sustain ecosystem function. We surveyed 102 pastures for RH and their associated managers during 2012–2013 across central Alberta, Canada. Pastures were generally diverse mixes of introduced grasses and contained few legumes, despite most (55%) being seeded to legumes. The majority of pastures were healthy (mean RH score = 78.8% ± 1.3%), with 2.9% having scores <50%. Criteria reducing scores were noxious weeds, bare soil, and evidence of erosion. Most pastures had a reported history of cultivation (75.5%), with those previously cultivated and seeded to introduced forage having greater stocking rates [6.18 ± 0.91 animal-unit-months (AUM) ha−1] compared with those lacking cultivation (2.14 ± 2.91 AUM ha−1). Farms with horses or mixed livestock tended to practice year-round continuous grazing and supplemental hay feeding. Stocking rates on continuous and rotationally grazed pastures were similar regardless of grazing season, with the highest stocking on pastures used year-round (19.54 ± 2.03 AUM ha−1) or throughout the dormant season (20.29 ± 3.10 AUM ha−1). Use of management inputs was variable, with manuring and harrowing common and fertilizing, over-seeding, and aerating infrequent. Herbicide use was reported on 15.7% of pastures, despite 83.3% containing noxious weeds. Use of prescribed fire was rare, although 36.3% of pastures had evidence of fire. Industrial disturbances were reported on 48.3% of pastures. Overall, these results indicate that these pastures experience complex management and the limited decline in RH is due to high stocking under year-long grazing.
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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 | 0.001 |
| 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".