Quantification and Simulation of Grazing Impacts on Soil Water in Boreal Grasslands
Bibliographic record
Abstract
Abstract We conducted a 2‐year study in central Alberta to quantify and simulate the soil water status of boreal grasslands under three grazing systems using wapiti (Cervus elaphus Canadensis), viz. (1) ungrazed control (UNG), (2) high intensity [4.16 animal unit month per ha (AUM) ha−1 ] short‐duration grazing (SDG) and (3) moderate intensity (2.08 AUM ha−1) continuous grazing (CG). Soil water was measured from May 1997 to September 1998 to a depth of 15 cm. Total annual precipitation in 1997 and 1998 was 494 and 429 mm respectively. In both years grazing treatments reduced soil water. Soil water content under SDG was significantly (P < 0.05) lower than CG. Simulation of soil water on each grazing system was conducted using PASTURE, a simple compartmental system dynamics model. Evaluation of the model was conducted using statistical criteria that included calculation of average error, root mean square, coefficient of residual mass and modelling efficiency and comparing these statistics against optimal values. Although the model under‐predicted soil water, simulations of soil water for grazing treatments in both years were closest to measured values with modelling efficiency (how well observed values are close to simulated values) up to 68 %.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".