Determination of ice production in a natural river: a case study in the Inner Mongolia Reach of the Yellow River
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".