MétaCan
Menu
Back to cohort
Record W4388529506 · doi:10.1080/07055900.2023.2277710

Winter Storm Activity across Canada at the End of the Century: A CMIP5 Multi-model Projection

2023· article· en· W4388529506 on OpenAlexafffundvenueabout
S. Basu, David Sauchyn

Bibliographic record

VenueATMOSPHERE-OCEAN · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Regina
FundersCompute CanadaUniversity of ReginaU.S. Department of Energy
KeywordsExtratropical cycloneStormEnvironmental scienceWinter stormClimatologyPrecipitationStorm trackSnowCyclone (programming language)Atmospheric sciencesMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

Extratropical cyclones are the main source of precipitation in most of Canada. They bring hazardous weather events like blizzards, heavy snow, freezing rain, etc. They also supply fresh water through spring snow melt and maintain the soil and surface water balance in sub-humid and semi-arid regions. Any future changes in extratropical cyclone activity over Canada may have a significant impact on agriculture and the local economies of the various regions. Future changes in regional storm activity over Canada have not been studied in detail. Therefore, we examined potential changes in extratropical storm activity by the end of the century using data from six models from CMIP5 under historical and future (RCP 8.5) emission scenarios. A statistical analysis of selected storm activity indices, using a storm identification and tracking algorithm, revealed a decrease in the number of storms over the mid-latitude regions of Canada. However, intense storms with a longer duration are projected over all regions at the end of the century. A further investigation of the physical mechanisms revealed that a decrease in the meridional temperature gradient over the mid-latitude regions and shifting of the vertical wind shear are responsible for these expected changes in storm activity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.610

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 science0.0000.001
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 head0.020
GPT teacher head0.250
Teacher spread0.230 · 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

Citations1
Published2023
Admission routes4
Has abstractyes

Explore more

Same venueATMOSPHERE-OCEANSame topicClimate variability and modelsFrench-language works237,207