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Record W2062431481 · doi:10.3137/ao.420405

On predicting maximum snowfall amounts in Alberta

2004· article· en· W2062431481 on OpenAlexafffundvenueabout
Max L. Dupilka, Gerhard W. Reuter

Bibliographic record

VenueATMOSPHERE-OCEAN · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSnowEnvironmental scienceClimatologyMeteorologyAtmospheric sciencesGeographyGeology

Abstract

fetched live from OpenAlex

Les chutes de neige superieures a 10 cm en Alberta sont habituellement associees a l'ascendance a grande echelle qui se produit a l'interieur d'une depression ondulatoire, et la quantite maximale de neige depend de la quantite maximale de vapeur d'eau disponible pour les precipitations. En vertu du principe de conservation de l'eau, la chute de neige maximale est liee au rapport de melange de saturation de la vapeur aux niveaux de la base et du sommet des nuages. Les incertitudes inherentes aux donnees d'entree permettent des approximations numeriques menant a une relation lineaire entre la chute de neige maximale et la temperature de la base des nuages. Pour tester la validite de la relation lineaire chute de neige-temperature, on a analyse la correlation entre les mesures des chutes de neige en 24 heures et les observations de temperature dans les sondages realises en amont. L'ensemble de donnees couvrait toute l'Alberta (sauf la region montagneuse de l'ouest) durant la periode d'octobre 1990 a avril 1993. Selon ces donnees, les quantites de neige semblent montrer une dependance approximativement lineaire avec la temperature a 850 mb, avec un coefficient de correlation de 0,62. Nous nous sommes aussi demandes si la relation chute de neige-temperature peut etre utilisee comme complement a la prevision quantitative des precipitations (PQP) employee dans les modeles de prevision meteorologique numerique (PMN). En particulier, la relation chute de neige-temperature s'est averee plus interessante que la sortie du modele du Centre europeen pour les previsions meteorologiques a moyen terme (CEPMMT). On discute des consequences de ces resultats sur la prevision des fortes chutes de neige en Alberta.

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 score0.065
Threshold uncertainty score0.999

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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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

Citations2
Published2004
Admission routes4
Has abstractyes

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