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Record W3095782647 · doi:10.1002/joc.6922

A classification scheme for identifying snowstorms affecting central New York State

2020· article· en· W3095782647 on OpenAlexaboutno aff
Justin J. Hartnett

Bibliographic record

VenueInternational Journal of Climatology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsWinter stormSnowStormClimatologyEnvironmental scienceLatitudeCyclogenesisAtmospheric sciencesMeteorologyGeologyGeographyCyclone (programming language)

Abstract

fetched live from OpenAlex

Abstract The Great Lakes region experiences anomalously high seasonal snowfall totals relative to similar latitudes. Although lake‐effect snowstorms are common in this region, snowfall occurs from a variety of storm types. This study examines snowstorms in a subsection of the Lake Ontario basin to develop a classification scheme to categorize the different types of snowstorms affecting the region. From 1985 to 2015, there were 11 different snowstorm types to affect the study area. The classification system was used to assess the frequency of, and snowfall produced by the different storm types within the eastern Great Lakes region. From the classification, snowstorms were categorized as either non‐direct cyclonic storms (NDCS) or direct cyclonic storms (DCS). Lake‐effect snowstorms, a type of NDCS, were the most frequent storm (35.1% of all storms) and accounted for approximately 39.4% of the snowfall. Most lake‐effect storms (37.7%) produced moderate snowfall totals (10.2–25.3 cm), yet heavy snowfall storms (≥25.4 cm) contributed significantly (ρ ≤ .05) more to seasonal snowfall totals than lighter snowfall storms. Direct cyclonic clippers forming over high latitudes of northwestern Canada, were the most frequent DCS in Central New York (11.3% of all storms), with nearly three quarters of the storms originating over Alberta. These storms only contributed 9.2% of the seasonal snowfall in the study area, compared to 12.7% from direct cyclonic Nor'easters forming near the east coast of North America. Although Nor'easters occur less frequently than clippers, when they do occur, they tend to produce heavy widespread snowfall across the region. The classification system proposed can be modified to accommodate snow basins across the globe. Classifying snowstorms will help determine the seasonal snowfall contribution from different storms and aid in future climate predictions, as individual snowstorm types may respond differently to a warming global climate.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.099
GPT teacher head0.332
Teacher spread0.233 · 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 designObservational
Domainnot available
GenreMethods

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

Citations14
Published2020
Admission routes1
Has abstractyes

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