MétaCan
Menu
Back to cohort
Record W2056463701 · doi:10.1175/jcli3571.1

Classification of Annual Great Lakes Ice Cycles: Winters of 1973–2002*

2005· article· en· W2056463701 on OpenAlexaboutno aff
Raymond A. Assel

Bibliographic record

VenueJournal of Climate · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsArctic ice packClimatologySea iceEnvironmental scienceAntarctic sea iceAnnual cycleCryosphereMelt pondContext (archaeology)Ice formationPhysical geographyOceanographyGeologyAtmospheric sciencesGeography

Abstract

fetched live from OpenAlex

Abstract Annual seasonal average ice cover from 1973 to 2002 and associated dates of first ice, last ice, and ice duration are presented and discussed. The annual seasonal average ice cover of each Great Lake is used to define three ice cycle classes: mild, typical, and severe. About half of the severe ice cycles occurred from 1977 to 1982 and about half of the mild ice cycles occurred from 1998 to 2002. The seasonal progression of daily lake-averaged ice cover, spatial differences in ice cover, and differences among the Great Lakes for mild, typical, and severe ice cycles are discussed within the context of lake bathymetry and winter air temperatures. Seasonal average ice cover is larger on Lakes Superior, Erie, and Huron relative to Lakes Michigan and Ontario, because of shallower depths (for Erie and Huron) and lower air temperatures (for Superior) relative to Lakes Michigan and Ontario. This ice cycle classification scheme can be used to compare future Great Lakes ice cycle severity with this 30-winter benchmark.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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
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

Citations79
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of ClimateSame topicArctic and Antarctic ice dynamicsFrench-language works237,207