Bibliographic record
Abstract
Remote acoustic observations of lunate megaripple migration are presented from two storm events during the Duck94 nearshore dynamics experiment for time periods during which longshore currents were weak (<20 cm/s). During these periods, significant wave orbital velocities were 50–80 cm/s; megaripple heights were 0.05–0.5 m; megaripple horns were directed shoreward; the crest‐to‐trough slope of the shoreward advancing ripple face was close to the angle of repose; and migration speeds were 10–40 cm/h onshore. The observations also indicate that the megaripples stalled, and may have begun to migrate offshore, when the mean offshore flow exceeded 20 cm/s during the second storm. Stress‐based bed load sediment transport models are moderately successful in predicting the observed dependence of migration velocity on measured fluid velocities separated into zero (mean current), infragravity wave, and sea‐and‐swell wave frequency bands. Wave and mean current friction factors, fw and fc, are obtained by best fit between the predicted and observed migration velocities, for two choices (3/2 and 5/2) for the stress exponent ξ in the bed load transport part of the model. Net transport is computed using wave velocity amplitudes determined from both run length statistics and wave by wave. Improved agreement with observations is obtained for the wave‐by‐wave net transport predictions and for the wave‐like treatment of the infragravity band. The level of agreement is relatively insensitive to the value of ξ. The best fit current and wave friction factor values, for ξ = 5/2 and 3/2 are fc = 4.3 × 10−3 and 8.0 × 10−3 and fw = 1.7 × 10−2 and 4.8 × 10−2, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".