Shallow water wave correction in the OEB (monochromatic and bichromatic waves)
Bibliographic record
Abstract
In this work first order and second order wave generation techniques are utilized to study the correct generation of mono and bichromatic waves in the OEB. For a flat bottom wave basin when the interaction between two frequencies are considered bounded sub harmonics or bounded low frequency waves are generated at the difference frequency and bounded super harmonics or bounded high frequency waves are generated at the sum frequency and they travel locked with their generating / fundamental wave components along with some unwanted free waves. These free waves are, free waves due to first order motion of the wave boards, free wave due to displacement of the wave board from its zero position and free wave due to local disturbances. These unwanted free waves are inevitable due to the linear motion of the wave maker. The second order wave generation technique includes second order board motion and correction components of the above-mentioned unwanted free waves. In this study only sub-harmonics or low frequency wave components are considered. Total 14 wave probes are used to capture the data in the wave tank. A NRC-IOT code (LWAVE) is used to isolate the primary waves, the bounded waves and the unwanted free waves from the measured data at each wave probe. The measured data are analyzed in this paper to illustrate the differences in the waves generated by two different generation techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".