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Record W3130868418 · doi:10.1080/07055900.2021.1877106

Construction of the Apparent Moisture Sink Index for the Movement of the South Asian High and Associated Indicative Significance

2021· article· en· W3130868418 on OpenAlexvenueno aff
Sidou Zhang, Shiyin Liu

Bibliographic record

VenueATMOSPHERE-OCEAN · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersYunnan UniversityNational Natural Science Foundation of China
KeywordsSink (geography)Index (typography)Movement (music)MoistureEnvironmental scienceGeologyPhysical geographyClimatologyGeographyMeteorologyCartographyComputer scienceArtAestheticsWorld Wide Web

Abstract

fetched live from OpenAlex

Previous studies indicate that the "spring flood" precipitation in northwestern Yunnan is closely related to the movement of the South Asian High (SAH) and the apparent moisture sink (Q2) in the southeastern Tibetan Plateau (TP). In this study, using 38 years of ERA-Interim daily- and monthly-mean grid data along with 35 years of Gongshan daily precipitation data, the correlation between the movement of the SAH and Q2 in the southeastern TP was analyzed. Then the Q2 index (QI) of the SAH movement was constructed, and its impact on the activities of the SAH was analyzed. The results show that the QI affects the vertical transport of latent heat by interfering with the "chimney" effect, which in turn affects the atmospheric vertical motion in the southeastern TP and adjacent areas. This affects the upper 100 hPa wind field, resulting in changes in wind speed and direction in the upper air, the distribution of the geopotential height field, and the intensity and position of the SAH centre. The QI is positively and negatively correlated with the longitude and latitude of the SAH centre, respectively, and positively correlated with the intensity, implying a predictive effect on the movement trend and intensity evolution of the SAH.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.226

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 studies0.0000.001
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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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