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Record W2076837662 · doi:10.1175/2010jamc2312.1

Climatological Conditions of Lake-Effect Precipitation Events Associated with the New York State Finger Lakes

2010· article· en· W2076837662 on OpenAlexaboutno aff
Neil F. Laird, Ryan A. Sobash, Natasha Hodas

Bibliographic record

VenueJournal of Applied Meteorology and Climatology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationEnvironmental scienceClimatologyPeriod (music)Atmospheric sciencesGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract A climatological analysis was conducted of the environmental and atmospheric conditions that occurred during 125 identified lake-effect (LE) precipitation events in the New York State Finger Lakes region for the 11 winters (October–March) from 1995/96 through 2005/06. The results complement findings from an earlier study reporting on the frequency and temporal characteristics of Finger Lakes LE events that occurred as 1) isolated precipitation bands over and downwind of a lake (NYSFL events), 2) an enhancement of LE precipitation originating from Lake Ontario (LOenh events), 3) an LE precipitation band embedded within widespread synoptic precipitation (SYNOP events), or 4) a transition from one type to another. In comparison with SYNOP and LOenh events, NYSFL events developed with the 1) coldest temperatures, 2) largest lake–air temperature differences, 3) weakest wind speeds, 4) highest sea level pressure, and 5) lowest height of the stable-layer base. Several significant differences in conditions were found when only one or both of Cayuga and Seneca Lakes, the largest Finger Lakes, had LE precipitation as compared with when the smaller Finger Lakes also produced LE precipitation. In addition, transitional events containing an NYSFL time period occurred in association with significantly colder and drier air masses, larger lake–air temperature differences, and a less stable and shallower boundary layer in comparison with those associated with solitary NYSFL events.

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 score0.034
Threshold uncertainty score0.397

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 studies0.0000.001
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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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

Citations28
Published2010
Admission routes1
Has abstractyes

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