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Record W1998882351 · doi:10.1139/l10-061

Projected climate conditions to 2100 for Regina, Saskatchewan

2010· article· en· W1998882351 on OpenAlexafffundvenueabout
Andrew deJong, Edward A. McBean, Bahram Gharabaghi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPrecipitationEnvironmental scienceEvapotranspirationClimatologyGeneral Circulation ModelGreenhouse gasClimate changeClimate modelAtmospheric sciencesRepresentative Concentration PathwaysMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

Possible trends of climate (temperature, precipitation, and potential evapotranspiration (PET)) for Regina, Saskatchewan are described, premised on comparisons of both projections from historical data and calculations from use of four general circulation models (GCMs). Results derived from GCMs of CGCM3.1, CCSM3, HadGEM1 and MIROC3.2, along with a series of storylines describing the relationships between the forces driving greenhouse gas and aerosol emissions during the 21st century, are employed which demonstrate increasing trends in temperature and precipitation. Unlike the identifiable and divergent projections of mean annual temperatures, there are relatively small differences in total annual precipitation projections using a number of projected scenarios of emissions. Further, although the projections indicate higher rates of precipitation are expected, there will not be increased water availability due to greater projected increases in PET, translating to there being less water available in the next century.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.535

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2010
Admission routes4
Has abstractyes

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