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Record W6980079442

Association between Arctic autumn sea ice concentration and early winter precipitation in China

2016· other· en· W6980079442 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicMarketing and Advertising Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationArcticArctic ice packArctic sea ice declineSea iceArctic geoengineeringArctic dipole anomaly
DOInot available

Abstract

fetched live from OpenAlex

Associations between autumn Arctic sea ice concentration (SIC) and early winter precipitation in China are studied using singular value decomposition analysis. The results show that a reduced SIC almost everywhere in the Arctic Ocean, except the northern Greenland Sea and Canadian Basin, are accompanied by dry conditions over central China, extending northeast from the Tibetan Plateau toward the Japan Sea, the Bohai Sea and the Yellow Sea, and wet conditions over South China and North China. Atmospheric circulation anomalies associated with SIC variability show two wave-train structures, which are persistent from autumn to winter, leading to the identified relationship between autumn Arctic SIC and early winter precipitation in China. Given that the decline in autumn SIC in the Arctic Ocean is expected to continue as the climate warms, this relationship provides a possible long-term outlook for early winter precipitation in China.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.388
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)0.0010.000

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 head0.223
Teacher spread0.216 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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