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Record W2917323381 · doi:10.1002/hyp.5772

Snow, frozen soils and permafrost hydrology in Canada, 1999–2002

2005· article· en· W2917323381 on OpenAlexaffabout
Ming‐ko Woo, Philip Marsh

Bibliographic record

VenueHydrological Processes · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSnowmeltPermafrostSnowHydrology (agriculture)Surface runoffEnvironmental scienceSnow fieldMeltwaterSoil waterSnowpackInfiltration (HVAC)GeologySnow coverGeomorphologyMeteorologySoil scienceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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.

Opus teacher head0.026
GPT teacher head0.218
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations54
Published2005
Admission routes2
Has abstractyes

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