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Record W2023790750 · doi:10.1175/jcli-3202.1

The Relationship between Snow Accumulation at Mt. Logan, Yukon, Canada, and Climate Variability in the North Pacific

2004· article· en· W2023790750 on OpenAlexaboutno aff
Summer Rupper, Eric J. Steig, Gerard H. Roe

Bibliographic record

VenueJournal of Climate · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersUniversity of Washington
KeywordsClimatologyStormAtmospheric circulationSnowIce coreTrough (economics)RidgeEnvironmental scienceWinter stormStorm trackCyclogenesisGeologyOceanographyCyclone (programming language)

Abstract

fetched live from OpenAlex

Abstract An ice core from Mt. Logan, Yukon, Canada, presents an opportunity to evaluate the degree to which ice core accumulation records can be interpreted as meaningful measures of interannual climate variability. Statistical analyses and comparisons with synoptic station data are used to identify the physical relationships between Mt. Logan ice core accumulation data and large-scale atmospheric circulation. These analyses demonstrate that only the winters of high accumulation years have a robust connection with atmospheric circulation. There are no consistent relationships during anomalously low and average accumulation years. The wintertime of high accumulation years is associated with an enhanced trough–ridge structure at 500 hPa and in sea level pressure over the northeast Pacific and western Canada, consistent with increased southerly flow bringing in warmer, moister air to the region. While both storm (i.e., 2–6 days) and blocking (i.e., 15–20 days) events project onto the same climate pattern, only the big storm events give rise to the dynamical moisture convergence necessary for anomalous accumulation. Taken together, these results suggest that while the Mt. Logan accumulation record is not a simple record of Pacific climate variability, anomalously high accumulation years are a reliable indicator of wintertime circulation and, in particular, of northeast Pacific storms.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.461
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.276
Teacher spread0.234 · 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

Citations21
Published2004
Admission routes1
Has abstractyes

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