MétaCan
Menu
Back to cohort
Record W3037711709 · doi:10.2166/wcc.2020.281

Synoptic climatological approach associated with three recent summer heatwaves in the Canadian Arctic

2020· article· en· W3037711709 on OpenAlexaffabout
Farahnaz Fazel‐Rastgar

Bibliographic record

VenueJournal of Water and Climate Change · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsYork University
Fundersnot available
KeywordsArcticClimatologyArctic ice packEnvironmental scienceArctic sea ice declineArctic geoengineeringSea iceArctic dipole anomalyAlbedo (alchemy)The arcticOceanographyAtmospheric sciencesGeologyAntarctic sea ice

Abstract

fetched live from OpenAlex

Abstract The observed unusually high temperatures in the Arctic during recent decades can be related to the Arctic sea ice declines in summer 2007, 2012 and 2016. Arctic dipole formation has been associated with all three heatwaves of 2007, 2012 and 2016 in the Canadian Arctic. Here, the differences in weather patterns are investigated and compared with normal climatological mean (1981–2010) structures. This study examines the high-resolution datasets from the North American Regional Reanalysis model. During the study periods, the north of Alaska has been affected by the low-pressure tongue. The maximum difference between Greenland high-pressure centre and Alaska low-pressure tongue for the summers of 2012, 2016 and 2007 are 8 hPa, 7 hPa and 6 hPa, respectively, corresponding and matching to the maximum summer surface Canadian Arctic temperature records. During anomalous summer heatwaves, low-level wind, temperatures, total clouds (%) and downward radiation flux at the surface are dramatically changed. This study shows the surface albedo has been reduced over most parts of the Canadian Arctic Ocean during the mentioned heatwaves (∼5–40%), with a higher change (specifically in the eastern Canadian Arctic region) during summer 2012 in comparison with summer 2016 and summer 2007, agreeing with the maximum surface temperature and sea ice decline records.

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 score0.128
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.071
GPT teacher head0.223
Teacher spread0.151 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Water and Climate ChangeSame topicArctic and Antarctic ice dynamicsFrench-language works237,207