Risk Models for Evaluation and Type Classification of Personal Flotation Devices
Bibliographic record
Abstract
This paper presents proposed models for assessing the aggregate performance of personal flotation devices (PFDs) by using risk methods. The aggregate performance is used to quantify the probability of a PFD saving lives following marine events. The models provide a formal structure and consistency to an approval process for new and novel engineering designs of such devices. They can also aid in identifying critical factors for evaluating the minimum level of performance necessary for approval. Such models could complement and enhance current standards and could result in significant safety improvements through the implementation of new technologies and designs. Such models could also aid in evaluating other new and innovative classes of engineering designs and designs for special needs. Also, they encourage creativity in system design by increasing the design domain and provide an overall performance measure allowing for trade-off analysis. The models can ultimately provide guidance in the development of future standards. The risk-based models consist of three recommended computational procedures for inherently buoyant, inflatable, and hybrid PFDs. Special panels of experts from the CORD Group, Canada, the U.S. Coast Guard (USCG), Underwriters Laboratories (UL), IMANNA Laboratories, Inc., and PFD Manufacturing Association (PFDMA) evaluated these models and provided recommended values by using formal expert opinion elicitation.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".