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Record W2071508688 · doi:10.3137/ao.440402

A study of drought characteristics over the Canadian prairies

2006· article· en· W2071508688 on OpenAlexafffundvenueabout
Erin Roberts, Ronald E. Stewart, Charles A. Lin

Bibliographic record

VenueATMOSPHERE-OCEAN · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPrecipitationEnvironmental scienceMoistureClimatologyCloud coverAtmospheric sciencesGeographyMeteorologyCloud computingGeology

Abstract

fetched live from OpenAlex

Abstract Droughts, a major concern to all of Canada, particularly to the Canadian Prairies, typically occur once every three years. Most research addressing such droughts has concentrated on the large‐scale conditions associated with this form of extreme weather whereas little research has been conducted on its smaller scale characteristics. The present study addresses these smaller scale issues. Several datasets (surface observations, drought indices, and precipitation data) were used to identify droughts occurring over the Prairies since 1953 and to compare their characteristics with those years associated with non‐drought conditions. Warm season, May to September, hourly surface observations (temperature, moisture, clouds, and precipitation type) from ten observation sites were compared for four categories of moisture across the Canadian Prairies, extreme drought, drought, non‐drought, and extreme wet conditions. When compared to non‐drought or extreme wet conditions, drought and extreme drought conditions are naturally associated with warmer temperatures and drier conditions; there is also little change in the amount of total cloud cover but cloud bases are higher and the maximum temperature occurs 1–2 h later in the afternoon. Near‐surface energy increases systematically from extreme wet to extreme drought conditions.

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 score0.322
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)0.0020.000

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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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

Citations19
Published2006
Admission routes4
Has abstractyes

Explore more

Same venueATMOSPHERE-OCEANSame topicHydrology and Drought AnalysisFrench-language works237,207