Grazing Effects on Snow Accumulation on Rough Fescue Grasslands
Bibliographic record
Abstract
Snow accumulation is an important process that defines the hydrological characteristics of grasslands and is mediated by vegetation structure. Grazing also affects those processes, but its relationship to snow accumulation is poorly understood. We conducted a study in the rough fescue grasslands in southwestern Alberta (lat 50 degrees11ʹ30 degreesN, long 113 degrees53ʹ30 degreesW) to determine the effect of grazing pressure on snow accumulation and its relationship with selected meteorological variables. Snow accumulation (mass per unit area) was measured throughout the winter from 1998 to 2004 within each of 3 watersheds that had different historical grazing pressures (high, moderate, and zero). In a second study, we examined the effect of artificially created patch sizes (0.5-, 1.0-, and 1.5-m diameter) on snow accumulation from 1998 to 2000. The yearly average of the heavily and moderately grazed watersheds was about 42% and 20%, respectively, less snow than the ungrazed watershed. Of the meteorological variables we tested, only average daily temperatures, average daily maximum temperatures, and snowfall were influenced by the watershed. Snowfall was about half as effective in predicting snow accumulation in the heavily grazed watershed as in the moderately grazed or ungrazed watersheds. Patch size was generally not effective, except at single observations in both 1998 and 1999 when the 1.0-m diameter patch captured the most snow mass per unit area. The ungrazed grassland captured a similar amount to that captured in the cut patches. The study indicates that increased grazing intensity reduces the ability of grasslands to capture snow.
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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 | 0.000 | 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".