Snow, frozen soils and permafrost hydrology in Canada, 1999–2002
Bibliographic record
Abstract
Abstract An overview is provided of Canadian research on snow, frozen soils and permafrost hydrology for 1999–2002, the period between the 1999 IUGG meeting in Birmingham and the 2003 IUGG in Sapporo. Snow research during this period emphasized the blowing snow and sublimation processes, the role of trees in snow distribution, and melt and the effect of heat advection on snowmelt, from patch to regional scales. Regional‐scale studies, largely in connection with the Mackenzie GEWEX study, examined the snow conditions of the lower Mackenzie basin and developed a coupled land surface scheme–hydrological model that incorporates snow processes. In frost hydrology, the effects of organic soils on runoff generation and flow delivery were given much attention. Field investigations ranged from plot to hillslope scales, and the results indicate that organic layers of high porosity permit the production of quick flow, even when frozen. Highly fractured bedrock in the Canadian Shield has likewise the effect of permitting snowmelt infiltration at below‐freezing temperatures. Finally, changes in snow‐covered areas and in snow equivalent over periods from a decade to a century were examined. The responses of snow and ground ice to the warm year of 1998 were also studied as an indication of hydrological responses to climatic warming. Copyright © 2005 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".