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Record W4366763412 · doi:10.1016/j.ejrh.2023.101390

Potential changes in climate indices in Alberta under projected global warming of 1.5–5 °C

2023· article· en· W4366763412 on OpenAlexaffabout
Hyung‐Il Eum, Babak Fajard, Tom Tang, Anil Gupta

Bibliographic record

VenueJournal of Hydrology Regional Studies · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of CalgaryAlberta Environment and Protected Areas
Fundersnot available
KeywordsGlobal warmingEnvironmental scienceClimatologyPrecipitationCoupled model intercomparison projectClimate changeMean radiant temperatureClimate modelAtmospheric sciencesGeographyMeteorologyGeologyOceanography

Abstract

fetched live from OpenAlex

Alberta province, Canada The global average surface temperature has continuously warmed at an unprecedentedly rapid rate since the mid-20th century. Employing CMIP6 (Coupled Model Intercomparison Project Phase 6) climate projections, this study suggested a comprehensive framework with state-of-the-art techniques and evaluated potential changes in climate indices under the GMT changes of + 1.5 °C, + 2 °C, + 3 °C, + 4 °C, and + 5 °C in Alberta, Canada. Main finding of this study is that a significant warming trend in annual mean temperature was projected from all of the selected CMIP6 climate projections in Alberta while there was no distinct trend in annual precipitation. Under the GMT changes from + 1.5 °C to + 5 °C, extreme cold temperature indices were warming at a larger rate in response to the GMT warming. In particular, the warming rate of the annual coldest minimum temperature in Alberta was 2.5 times faster than GMT warming. In addition, a potential decrease in summer precipitation was projected under the GMT warming, leading to a drier and warmer summer in the central and southern parts of Alberta. Furthermore, more extreme drought conditions were projected in Alberta under the GMT warming, indicating that the extreme drought conditions are likely to become more common in Alberta along with the GMT warming.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.836

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.045
GPT teacher head0.312
Teacher spread0.268 · 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 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

Citations15
Published2023
Admission routes2
Has abstractyes

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