MEASURING ENERGY DISSIPATION RATES IN A WAVE TANK
Bibliographic record
Abstract
ABSTRACT The effectiveness of dispersants is typically evaluated at various scales ranging from the smallest (10 cm, typical of flask tests in the laboratory) to the largest (10's to 100's of meters, typical of field scale open water dispersion tests). This study aims at evaluating dispersant effectiveness at intermediate or pilot scale. The hypothesis is that the energy dissipation rate per unit mass, ɛ, plays a major role in the effectiveness of a dispersant. Therefore, it is stipulated that in fairly general conditions, conservation of ɛ between the wave tank scale and that of the field scale is sufficient to accurately evaluate the effectiveness of a dispersant to disperse oil droplets. A wave tank measuring 16 m long x 0.6 m wide x 2 m deep was constructed on the premises of the Bedford Institute of Oceanography, Halifax, Nova Scotia. Waves were generated using a flap-type wave maker. Conditions of the breaking waves were created using a dispersive focusing technique in which the wave maker is started at high frequency and then the frequency decreased to create breaking waves. Experiments defining the velocity profile and energy dissipation rates in the wave tank were conducted at 2 different induced breaking-wave energies. Energy in the wave tank was measured with an Acoustic Doppler Velocimeter (ADV) coupled to a data acquisition system. Energy in the lab flasks was measured with a Hot Wire Anemometer.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".