Hull vibration analysis of a small multipurpose fishing vessel from Newfoundland and Labrador
Bibliographic record
Abstract
Regarded as one of the most dangerous industries for workers, commercial fishing is a high-risk industry that provides a living for millions around the world. It is imperative for designers to analyze and obtain practical solutions for the reduction of these unnecessary hazards. The need to quantify and analyze the risk areas onboard fishing vessels has been pressed by authorities worldwide from the increasing number of injuries and fatalities in this industry. Fishing vessels are mainly known for their high levels of vibrations due to their layout and relatively small size. Vibration mitigation on fishing vessels impacts both vessel equipment and onboard crew. Benefits of reduction include protection of sensitive ship equipment and hydro-acoustic apparatus, low noise emitted to the water so as not to scare fish schools, and increased safety of the onboard crew. Fish harvesters working in these vessels are in constant prolonged exposure causing a decrease in comfort levels leading to an unsafe work environment. The approach of this study is to effectively capture the dynamics of a case study fishing vessel in terms of vibrations, providing a practical methodology for designers to implement.
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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 | 0.001 | 0.001 |
| 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".