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Record W4386607647 · doi:10.2166/wcc.2023.203

Discriminant analysis of the freeze-up and break-up conditions in the Inner Mongolia Reach of the Yellow River

2023· article· en· W4386607647 on OpenAlexaff
Zhixing Hou, Jun Wang, Jueyi Sui, Guowei Li, Baosen Zhang, Liangguang Zhou

Bibliographic record

VenueJournal of Water and Climate Change · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Northern British Columbia
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsIce formationInner mongoliaVolume (thermodynamics)Channel (broadcasting)Downstream (manufacturing)Environmental scienceUpstream (networking)Hydrology (agriculture)GeologyAtmospheric sciencesGeographyGeotechnical engineeringEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract This study divided the total storage potential in a natural channel into the ice production volume and the water storage capacity volume. Thermal factors, hydraulic processes, topography, and ice formation were selected to derive a discriminant equation for freeze-up and break-up conditions in the Inner Mongolia Reach of the Yellow River. The trends observed from data for the freeze-up dates, break-up dates, and total frozen days from 2017 to 2020 conform to the principle that the river is gradually frozen from the downstream to the upstream and later thawed from the upstream to the downstream. The number of frozen days in the downstream is greater than in the upstream. Results indicate that freeze-up typically occurs when the proportion of ice in the channel is relatively high. Higher temperatures and greater discharges are required to facilitate the break-up of the river when the equilibrium ice thickness is greater. This study can provide a theoretical basis and framework for establishing an accurate freeze-up and break-up forecast model to prevent and mitigate ice-induced disasters.

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 score0.020
Threshold uncertainty score0.182

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)0.0000.000

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 head0.028
GPT teacher head0.240
Teacher spread0.212 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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