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Record W2076187582 · doi:10.1002/hyp.6117

Snowmelt runoff processes in a headwater lake and its catchment, subarctic Canadian Shield

2006· article· en· W2076187582 on OpenAlexaffabout
Corrinne Mielko, Ming‐ko Woo

Bibliographic record

VenueHydrological Processes · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSnowmeltSurface runoffHydrology (agriculture)MeltwaterSubarctic climateOutflowSnowEnvironmental scienceDrainage basinStreamflowGeologyWater yearGeomorphologyGeographyOceanographyEcology

Abstract

fetched live from OpenAlex

Abstract During the snowmelt season, surface runoff into northern Canadian Shield lakes is governed by the distribution of snow and its ablation, storage in catchment slopes and valleys, and flow delivery from uplands, bottomlands and from upper lakes. For the lake studied, time lags exist between the commencement of snowmelt and the arrival of runoff to the lake, with evaporation from the uplands and bottomlands consuming some of the meltwater produced. Inflow from an upper lake is delayed. With all these inputs, the lake becomes flooded, first forming a moat, followed by an expansion of the open water areas, which are subject to evaporation loss. Lake outflow begins when water level rises above the outlet threshold. For the snowmelt season, outflow constitutes 40% of total melt and rainfall. The lake itself is a major storage that continues to maintain outflow until the lake level drops below the threshold in early summer, after which flow ceases. Snowmelt is the main period when runoff is generated from the headwater catchment to replenish storage depleted in the previous summer, and to produce flow along the Shield valley that contains a chain of lakes. Copyright © 2006 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 score0.589
Threshold uncertainty score1.000

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)0.0010.000

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 head0.013
GPT teacher head0.215
Teacher spread0.203 · 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

Citations52
Published2006
Admission routes2
Has abstractyes

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