MétaCan
Menu
Back to cohort
Record W1520572508 · doi:10.1002/joc.4170

Extratropical cyclone climatology across eastern Canada

2014· article· en· W1520572508 on OpenAlexaffabout
Mathieu Plante, Seok‐Woo Son, Eyad H. Atallah, John R. Gyakum, Kevin M. Grise

Bibliographic record

VenueInternational Journal of Climatology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
Fundersnot available
KeywordsExtratropical cycloneClimatologyTropical cycloneEast coastCyclone (programming language)Environmental scienceEl Niño Southern OscillationCyclogenesisOceanographyGeology

Abstract

fetched live from OpenAlex

ABSTRACT Extratropical cyclone ( ETC ) tracks across eastern Canada are examined by applying a Lagrangian tracking algorithm to the lower‐tropospheric relative vorticity field of reanalysis data. Both the seasonal cycle and the interannual variability of ETCs are quantified in terms of overall cyclone frequency, intensity, and regions of development and decay. We find that ETCs travelling to eastern Canada tend to develop over the Rockies, the Great Lakes and the US East Coast. The ETCs are most intense over Newfoundland and the North Atlantic Ocean, confirming previous findings. While ETCs at cities along the Atlantic coastline (e.g. St. John's) are dominated by East Coast cyclones (which are intense in winter), those inland (e.g. Toronto) track primarily from the Great Lakes. ETCs that develop over the Gulf of Mexico affect eastern Canada infrequently, but those that do tend to be intense. The interannual variability of the wintertime ETCs is influenced by the El Niño‐Southern Oscillation ( ENSO ). Significant ENSO ‐related variability is found over most regions of southern Canada, except on the east coast. Although ETCs at Toronto are significantly modulated by ENSO , no visible changes are found at St. John's. These ENSO ‐related ETC changes are mostly due to the shifts in ETC development regions, with minor changes in the travelling direction of ETCs .

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.280
Teacher spread0.267 · 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.

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

Citations32
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of ClimatologySame topicClimate variability and modelsFrench-language works237,207