Modeling Paravanes for Seakeeping Tests of Fishing Vessels
Bibliographic record
Abstract
In a recent study to evaluate the occupational risks induced by ship motions on the fishing fleet of Newfoundland, a model of a 65’ fishing vessel fitted with paravanes was tested in the Offshore Engineering Basin (OEB) of IOT. This project was done under a larger umbrella community initiative called SafetyNet to evaluate health and safety issues of the fishing industry. The particular focus of this project was to evaluate motion induced interruptions onboard the fishing vessels for various operational scenarios. Paravanes are the most common motion reducing devices used on Newfoundland fishing vessels. Paravanes were initially modelled by scaling down both the geometry and the weight using Froude scaling laws. This modeling strategy proved to be unsuccessful and several modifications to the paravanes were required throughout the test program. This paper describes the model seakeeping tests done at IOT with particular focus on the challenges encountered in modeling the paravanes. Some of the model test, sea trial and numerical prediction results obtained with and without paravanes deployed are included in the paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".