Flow patterns and critical criteria of thermally stratified shear flow in braided rivers
Bibliographic record
Abstract
Flow characteristics of thermally stratified shear flow in braided rivers are particularly complicated and poorly understood. In this study, a series of typical flow patterns was examined and their critical criteria were determined. Four flow patterns were identified: mixed, locally unstable, continuously stratified, and two-layer flow. Temperature distributions of the four types of flow patterns were analyzed and compared. The critical Froude numbers for unstable flow, F Dcr1 , and stable flow, F Dcr2 , were determined to be 6 and 1, respectively, and comparison of F Dcr1 and F Dcr2 to the peak Froude numbers , F D1 at the outer bank and F D2 at the inner bank along the anabranch, allowed the flow patterns to be assessed. Then, a discriminant based on initial Jeffreys-Keulegan stability parameters was established to distinguish the flow stages from two-layer flow to completely mixed flow . It is indicated that the three critical Jeffreys-Keulegan parameters increased with the diversion angle of braided rivers. Results also show that, compared to the stratified flow in straight and curved channels, it was more difficult for braided stratified flow to maintain as two-layer flow, and it more easily became mixed flow. Consequently, empirical expressions for stability criteria of the thermally stratified shear flow in braided rivers are presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".