Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hyp.6125 Intra-basin variability of snowmelt water balance calculations in a subarctic catchment
Bibliographic record
Abstract
The intra-basin variability of snowmelt and melt-water runoff hydrology in an 8 km2 subarctic alpine tundra catchment was examined for the 2003 melt period. The catchment, Granger Creek, is within the Wolf Creek Research Basin, Yukon, which is typical of mountain subarctic landscapes in northwestern Canada. The study catchment was segmented into nine internally uniform zones termed hydrological response units (HRUs) based on their similar hydrological, physiographic, vegetation and soil properties. Snow accumulation exhibited significant variability among the HRUs, with greatest snow water equivalent in areas of tall shrub vegetation. Melt began first on southerly exposures and at lower elevations, yet average melt rates for the study period varied little among HRUs with the exception of those with steep aspects. In HRUs with capping organic soils, melt water first infiltrated this surface horizon, satisfying its storage capacity, and then percolated into the frozen mineral substrate. Infiltration and percolation into frozen mineral soils was restricted where melt occurred rapidly and organic soils were thin; in this case, melt-water delivery rates exceeded the frozen mineral soil infiltration rate, resulting in high runoff rates. In contrast, where there were slower melt rates and thick organic soils, infiltration was unlimited and runoff was suppressed. The snow water equivalent had a large impact on runoff volume, as soil storage capacity was quickly surpassed in areas of deep snow, diverting the bulk of melt water laterally to the drainage network. A spatially distributed water balance indicated that the snowmelt freshet was primarily controlled by areas with tall shrub vegetation that accumulate large quantities of snow and by
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.656 | 0.645 |
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".