Evaluating peak flow sensitivity to clear‐cutting in different elevation bands of a snowmelt‐dominated mountainous catchment
Bibliographic record
Abstract
A hydrologic model of the mountainous snowmelt‐dominated Redfish Creek catchment (British Columbia) is used to evaluate Interior Watershed Assessment Procedure (IWAP) guidelines regarding peak flow sensitivity to logging in different elevation bands of a basin. Simulation results suggest that peak flow increases are caused by greater snow accumulation and melt in clear‐cut areas while similar evapotranspiration rates are predicted under forested and clear‐cut conditions during spring high flow. Snow accumulation and melt are clearly related to elevation, but the relationship between logging elevation and peak flow change is more complex than perceived in the IWAP. Logging in the bottom 20% of the catchment causes little or no change in peak flow because of the small low‐elevation snowpack and the timing of snowmelt, while clear‐cut area alone appears to be a good indicator of peak flow increases due to logging at higher elevation. Temporal variability in peak flow changes due to clear‐cutting is substantial and may depend more on temperatures during snowmelt than on the size of the snowpack. Long‐term simulations are needed to improve quantitative estimates of peak flow change while the importance of watershed topographic characteristics for snowmelt and peak flow generation must be further examined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 |
| 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 teacher head, 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".