Evaluation of sand transport models by in situ observations.
Bibliographic record
Abstract
Sand transport rates measured from nine deployments of an annular benthic flume off the western Newfoundland coast and Sable Island Bank (Canada), are compared with predictions using: (1) Engelund and Hansen's total load equation; (2) Einstein-Brown's bedload equation; (3) Bagnold's total load equation, modified by Gadd et al.; and (4) Yalin's bedload equation. TheD grain diameters examined were: 0.19mm(15 trials); 0.33mm(37 trials); and 0.50 mm (7 trials). Results showed that equation (1) under-predicted in about a 40% for all the grain sizes. Equations (2) and (4) showed almost the same range of under-prediction: approximately 30% for D = 0.19 mm, 40% for D = 0.33 mm and 50% for D = 0.5 mm. The maximum variations on the observed-predicted ratios were obtained by equation (3), over-predicting by a factor of 2.45 and 1.9 for D = 0.19 mm and D = 0.33 mm, respectively, and under-predicting by a factor of 0.42 for D = 0.5 mm, but showing the highest correlations among all the tested models (R = 0.96 and 0.8 forD = 0.19mmand 0.33 mm, respectively). These variations are related to the inclusion of outliers in the original computation of the grain size dependent -parameter employed by equation (3). Based on the obtained results and through the analysis of published data, a significant improvement on the formulation (3) is suggested.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".