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Record W4293561568 · doi:10.2166/wcc.2022.184

Determination of ice production in a natural river: a case study in the Inner Mongolia Reach of the Yellow River

2022· article· en· W4293561568 on OpenAlexaff
Zhixing Hou, Jun Wang, Jueyi Sui, Baosen Zhang, Fangxiu Zhang

Bibliographic record

VenueJournal of Water and Climate Change · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Northern British Columbia
FundersNational Defense Science and Technology Innovation Fund of the Chinese Academy of SciencesNational Natural Science Foundation of China
KeywordsEnvironmental scienceInner mongoliaHydrology (agriculture)Heat fluxFlux (metallurgy)Atmospheric sciencesIce formationHeat transferClimatologyGeologyGeographyMechanicsMaterials science

Abstract

fetched live from OpenAlex

Abstract In the present study, ice production in a natural river reach has been studied by means of the thermodynamic theory regarding the heat flux between ice, air, riverbed, and water. The heat transfer coefficient and equivalent total heat flux were determined for different periods during winter. The characteristics of variation and distribution of ice production in the Inner Mongolia Reach of the Yellow River (IMRYR) were studied in combination with the change of heat flux. A model for describing the temporal–spatial variation of ice production for the IMRYR has been developed. The ice production process of the IMRYR from 2017 to 2021 was simulated using the proposed model, and the simulation results were in good agreement with those of the measurements. Results of the analysis showed that when the total ice production in the Bayangaole gauging station reaches 3.18 × 107 m3, a freeze-up process in this river reach is likely to occur. The influence degree of each variable on the ice production was in the following descending order: water surface area, air temperature, radiation, and flow. Particularly, a change of 20% of the water surface area will lead to a 11.48% change in the final calculated result of ice production.

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)0.001
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 score0.050
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.024
GPT teacher head0.236
Teacher spread0.213 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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