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Record W2004219568 · doi:10.1002/hyp.371

Significance of ice‐induced storage to spring runoff: a case study of the Mackenzie River

2002· article· en· W2004219568 on OpenAlexaff
Terry D. Prowse, T. Carter

Bibliographic record

VenueHydrological Processes · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsSnowmeltSpring (device)HydrographHydrology (agriculture)Environmental scienceMeltwaterSurface runoffWater storageGeologySnowGeomorphologyInletGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract For most ice‐covered rivers at northern latitudes, the spring freshet is often the largest hydrologic event of the year. Based on a case study of the Mackenzie River, water released from hydraulic storage due to flow abstraction during the early winter is shown to be a major component of the flow volume that comprises the following spring freshet. The amount of water placed into hydraulic and ice‐growth storage over a 60‐day period was calculated to be equal to 27% of the flow that would normally have occurred during this period if an ice cover had not formed. The amount of water released from this ice‐induced hydraulic storage at the time of break‐up and the concomitant spring snowmelt peak accounted for 15 to 19% of the spring freshet volume, depending on the temporal definition of the ‘release’ period. The ice‐related contribution increases to as much as 25% of freshet volume if the ice growth during the fall depression is also included. Importantly, even this percentage is considered an underestimate since it does not take into account the flow contribution from the melt of the complete winter ice cover. If the ice‐induced storage volume is not accounted for in the spring hydrograph, then the volumes of water from spring snowmelt and/or rainfall would be significantly overestimated. Copyright © 2002 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.718

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 head0.037
GPT teacher head0.231
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations56
Published2002
Admission routes1
Has abstractyes

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