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Record W178357289

Runoff modelling within the Canadian Regional Climate Model (CRCM): analysis over the Quebec/Labrador watersheds.

2009· article· en· W178357289 on OpenAlexaboutno aff
Biljana Music, Anne Frigon, Michel Slivitzky, André Musy, D. Caya, René Roy

Bibliographic record

VenueIAHS-AISH publication · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSurface runoffWatershedEnvironmental scienceClimate modelHydrology (agriculture)GeographyClimate changePhysical geographyGeology
DOInot available

Abstract

fetched live from OpenAlex

This study focuses on evaluation of the hydrological performance of the Canadian Regional Climate Model (CRCM) coupled to the Canadian Land Surface Scheme (CLASS). The CRCM's ability to adequately simulate annual mean runoff over 21 small watersheds in the Quebec/Labrador peninsula is assessed over the period 1961―1999. Since runoff is a spatial and temporal integrator of weather events, it represents a very useful variable for climate model validation, especially in areas where conventional surface weather observations are scarce. In addition, the sensitivity of simulated runoff to domain size and lateral boundary conditions is investigated. Results of the analysis indicate that CRCM tends to systematically underestimate observed annual mean runoff over most of the investigated watersheds. It was found that choice of simulation domain has a considerable effect on the simulated hydrological regime at the watershed scale. Different re-analyses used as driving data have less influence than domain size. However it may be important (larger than CRCM's internal variability) when simulations are performed over a relatively small domain.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.223
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations3
Published2009
Admission routes1
Has abstractyes

Explore more

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