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Record W3011692907 · doi:10.1111/1752-1688.12831

Wintertime Surface Heat Exchange for the Inner Mongolia Reach of the Yellow River

2020· article· en· W3011692907 on OpenAlexaboutno aff
Shuixia Zhao, Hung Tao Shen, Xiaohong Shi, Changyou Li, Chao Li, Shengnan Zhao

Bibliographic record

VenueJAWRA Journal of the American Water Resources Association · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsHeat fluxEnvironmental scienceAtmospheric sciencesInner mongoliaHeat exchangerWind speedLatitudeMeteorologyHeat transferClimatologyThermodynamicsGeologyPhysicsChinaGeography

Abstract

fetched live from OpenAlex

Abstract This study analyzed the wintertime surface heat exchange for the Inner Mongolia reach of the Yellow River, China, based on the data from the nearby weather station at Wulateqianqi. In this analysis, the solar radiation is based on the observed data. Other components of the surface heat flux, that is, long‐wave radiation, and evaporative and conductive heat fluxes, are calculated. The relative importance of the contributions of long‐wave radiation, conductive, and evaporative heat fluxes are in descending order. The air temperature is the most important meteorological factor to the total heat flux. Although the wind speed influences evaporative and conductive heat fluxes, it has the least correlation with the total heat budget. The heat exchange coefficient for the linearized surface heat exchange equation is 21.87 W/(m2 °C), which is comparable with published values in the regions of United States and Canada with similar latitudes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.077
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.190
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2020
Admission routes1
Has abstractyes

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