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Record W7020939825

A Meteorological Analysis of Important Contributions to the 1999-2005 Canadian Prairie Drought

2013· article· en· W7020939825 on OpenAlexaboutno aff

Bibliographic record

VenueScholarly Commons (Embry–Riddle Aeronautical University) · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNatural hazardVulnerability (computing)PrecipitationHazardSubsidenceNatural disasterArctic
DOInot available

Abstract

fetched live from OpenAlex

Drought is a complex natural hazard that is endemic to the Canadian prairies. The 1999–2005 Canadian prairie drought, which had great socioeconomic impacts, was meteorologically unique in that it did not conform to the traditional persistent positive Pacific–North American (PNA) pattern and west coast ridging paradigm normally associated with prairie drought. The purpose of this study is to diagnose the unique synoptic-scale mechanisms responsible for modulating subsidence during this drought. Using 30-day running means of the percent of normal precipitation from station data, key severe dry periods during 1999–2005 are identified. Analysis of the mean fields from reanalysis data shows that these dry events can be grouped into three upper-level flow categories: amplified warm, amplified cold, and zonal. Amplified warm cases match the traditional ridging paradigm, while amplified cold and zonal cases elucidate the fact that cold-air advection and downsloping flow, respectively, can also be important subsidence mechanisms during a Canadian prairie drought. In all, the 1999–2005 drought was more meteorologically complex on the synoptic scale than previous historic prairie droughts. Finally, a brief historical perspective shows that the drought was centered in 2001–02 and was not as severe as historical droughts, suggesting that societal vulnerability also played a substantial role in the impacts of the 1999–2005 drought.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.011
GPT teacher head0.217
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; both teacher heads agree on what is shown here.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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