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Record W1972141198 · doi:10.1109/oceans.2014.7003297

Assessment of life saving appliances regulatory requirements — Human factors knowledge gaps

2014· article· en· W1972141198 on OpenAlexaffabout
António Simões Ré

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsCommunity Sector Council Newfoundland and Labrador
Fundersnot available
KeywordsLife savingWork (physics)Human lifeRisk analysis (engineering)Accident (philosophy)BusinessOperations managementEngineeringMedical emergencyMedicine

Abstract

fetched live from OpenAlex

Life saving appliances are used throughout Canada and around the world every day by a large number of individuals who work or travel over open water. Personnel rely on these life saving appliances to help provide protection from harsh environments, and reduce the risk of injury or death in the event of a marine accident. Due to their importance in helping to save lives at sea, life saving appliances are built and tested according to specific standards and regulations to ensure that they provide the level of performance required. Unfortunately, life saving appliances do not always perform as expected which can lead to unexpected injuries or loss of life. Given that life saving appliances must meet specific performance goals as prescribed by standards and regulations, it is often these goals that fall short of what is actually needed during a marine accident. A knowledge gap is created when the testing conditions, as outlined in a standard or regulation, do not accurately reflect those conditions found during a marine accident. As a result, a life saving appliance will often meet performance goals that are below those required to prevent an injury or loss of life during an actual marine accident. The Canadian regulation: “Life Saving Equipment Regulations” C.R.C., c. 1436 was reviewed and possible knowledge gaps with respect to human factors were identified. The goals and requirements for life saving appliances in the regulation were compared against existing work done in the area of marine safety to determine if what was prescribed adequately reflected what could be found during a marine accident. There were many gaps identified in the regulation, commonly caused by prescriptive wording specifying conditions not commonly found during a marine accident. These knowledge gaps will widen as conditions become more severe than what is prescribed in the regulations possibly leading to even further decrease in life saving appliance performance than what is already measured.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.066
metaresearch head score (Gemma)0.131
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.496
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.131
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.008
Science and technology studies0.0030.002
Scholarly communication0.0060.003
Open science0.0040.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.226
GPT teacher head0.549
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes2
Has abstractyes

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