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2024· peer-review· en· W4401456184 on OpenAlexaboutno aff
Thomas Ballinger

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryEnvironmental science

Abstract

fetched live from OpenAlex

<strong class="journal-contentHeaderColor">Abstract.</strong> Today&rsquo;s Arctic is characterized by a lengthening of the sea ice melt season, but also by fast and at times unseasonal melt events. Such anomalous melt cases have been identified in Pacific and Atlantic Arctic sector sea ice studies. Through observational analyses, we document an unprecedented, simultaneous marginal ice zone melt event in the Bering Sea and Labrador Sea in March of 2023. Taken independently, variability in the cold season ice edge at synoptic time scales is common. However, such anomalous, short-term ice loss over either region <em>during the climatological sea ice maxima</em> is uncommon, and the tandem ice loss that occurred qualifies this as a rare event. The atmospheric setting that supported the unseasonal melt events was preceded by a sudden stratospheric warming event that, along with ongoing La Ni&ntilde;a teleconnections, led to positive tropospheric height anomalies across much of the Arctic and the development of anomalous mid-troposphere ridges over the ice loss regions. These large-scale anticyclonic centers funneled extremely warm and moist airstreams onto the ice causing melt. Further analysis identified the presence of atmospheric rivers within these warm airstreams whose characteristics likely contributed to this bi-regional ice melt event. Whether such a confluence of anomalous wintertime events associated with troposphere-stratosphere coupling may occur more often in a warming Arctic remains a research area ripe for further exploration.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.476
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.320
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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