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Record W3198689306 · doi:10.1016/0967-0653(95)91416-2

10.1016/0967-0653(95)91416-2

2000· article· en· W3198689306 on OpenAlexvenueno aff
Luo Hui-bang

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Changes in China
Canadian institutionsnot available
Fundersnot available
KeywordsClimatologyMonsoonGeopotential heightSea surface temperatureNorthern HemisphereAnomaly (physics)GeologyEast Asian MonsoonEast AsiaEnvironmental scienceSiberian HighChinaOceanographyPrecipitationGeographyMeteorology

Abstract

fetched live from OpenAlex

In this paper the relationships between the sea surface temperature (SST) of Xisha and that in the northern Indian and northern Pacific Oceans,the geopotential height at 500 hPa level of the Northern Hemisphere,and rainfall in China are studied statistically using data in the period of 1961-1992.Results show that in winter,the interannual variation in SST of Xisha describes that for a large oceanic region off the East Asia coast,and is closely related to the activity of East Asia winter monsoon.On the other hand,there exist very high values of auto-correlation of Xisha SST anomaly from December through the following July,but the anomalous condition is hardly correlated to that in the preceding autumn.The winter monsoon related anomalous SST condition in Xisha has a strong tendency to persist through the succeeding summer monsoon season with the same sign.In addition,correlation maps of monthly mean rainfall in China with respect to Xisha SST of the same month show positive correlations with confidence level above 95% to the east of 110°E and to the south of Changjiang (Yangtze) River during the months of October through April;the region becomes smaller in May and changes correlation sign in June;the positive correlation region is located in the middle and lower reaches of Changjiang River from July to September.The air-sea interaction plays an important role in these processes.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.653
Threshold uncertainty score0.820

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)1.0001.000

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 head0.006
GPT teacher head0.169
Teacher spread0.163 · 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
Published2000
Admission routes1
Has abstractyes

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