Robust classification for the joint velocity‐intermittency structure of turbulent flow over fixed and mobile bedforms
Bibliographic record
Abstract
ABSTRACT Two datasets of turbulence velocities collected over different bedform types under contrasting experimental conditions show similarity in terms of velocity‐intermittency characteristics and suggest a universality to the velocity‐intermittency structure for flow over bedforms. One dataset was obtained by sampling flow over static bedforms in different locations, and the other was based on a static position but mobile bedforms. A flow classification based on the velocity‐intermittency behaviour is shown to reveal some differences from that based on an analysis of Reynolds stresses, boundary layer correlation and turbulent kinetic energy. This may be attributed to the intermittency variable, which captures the local effect of individual turbulent flow structures. Locations in the wake region or the outer layer of the flow are both shown to have a velocity‐intermittency behaviour that departs from that for idealized wakes or outer layer flow because of the superposition of localized flow structures generated by bedforms. The combined effect of this yields a velocity‐intermittency structure unique to bedform flow. The use of a time series of a single velocity component highlights the potential power of our approach for field, numerical and laboratory studies. The further validation of the velocity‐intermittency method for non‐idealized flows undertaken here suggests that this technique can be used for flow classification purposes in geomorphology, hydraulics, meteorology and environmental fluid mechanics. © 2014 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".