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Laurentian Great Lakes Ice and Weather Conditions for the 1998 El Niño Winter<sup>*</sup>

2000· article· en· W2014850044 on OpenAlexaboutno aff
Raymond A. Assel, John E. Janowiak, D Boyce, C. O'Connors, Frank H. Quinn, David C. Norton

Bibliographic record

VenueBulletin of the American Meteorological Society · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsWinter stormClimatologyPrecipitationStormEnvironmental scienceShelf icePhysical geographyGeologyOceanographyArctic ice packSea iceGeographyAntarctic sea iceMeteorology

Abstract

fetched live from OpenAlex

Winter 1997/98 occurred during one of the strongest warm El Niño events, and the Great Lakes experienced one of the least extensive ice covers of this century. Seasonal maximum ice cover for the combined area of the Great Lakes was the lowest on record (15%) relative to winters since 1963, a distinction formerly held by winter 1982/83 (25%), which was also an exceptionally strong El Niño winter. Maximum ice covers set new lows in winter 1997/98 for Lakes Erie (5%), Ontario (6%), and Superior (11%), tied the all-time low for Lake Huron (29%), and came close to tying the all-time low on Lake Michigan (15%; all-time low is 13%). Here the authors compare seasonal progression of lake-averaged ice cover for winter 1982/83, winter 1997/98, and a 20-winter normal (1960–79) derived from the NOAA Great Lakes Ice Atlas and discuss the 1997/98 ice cover in detail. Winter air temperatures in the Great Lakes were at or near record high levels, storms were displaced farther to the south over eastern North America, and precipitation was below average in the northern portion of the Great Lakes region. The Northern Hemispheric synoptic flow patterns responsible for this winter weather, the Great Lakes winter severity over the past two centuries, and impacts of this mild winter are briefly discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.993

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.009
GPT teacher head0.217
Teacher spread0.209 · 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 designNot applicable
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
Published2000
Admission routes1
Has abstractyes

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