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Record W4301257129 · doi:10.5957/icetech-2008-119

Transport Canada EER Research and Development Program

2008· article· en· W4301257129 on OpenAlexaffabout
Frank G. Bercha, Ernst Radloff, Fred Leafloor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsTransport Canada
Fundersnot available
KeywordsComputer scienceWork (physics)Submarine pipelineSelection (genetic algorithm)Scale (ratio)Phase (matter)Systems engineeringEngineering managementOperations researchRisk analysis (engineering)EngineeringBusiness

Abstract

fetched live from OpenAlex

The paper describes a multiyear offshore installation Escape, Evacuation, and Rescue (EER) research and development program carried out from 2000 to 2007. The general objective of the work was to develop performance-based design standards and guidelines for optimal EER systems for installations in Canadian waters. Phase 1 involved developing a risk and performance evaluation tool, reviewing existing regulations, implementing recent Ocean Ranger recommendations, and conducting various applied research programs including those on human performance in EER. Phase 2 work focused on developing preliminary performance-based standards that can be used by offshore regulators for the selection of evacuation systems. Phase 3 involved further refinement of the standards based on model and full-scale testing and computer simulation. While standards ultimately developed as a result of this research are intended to be applied nationally in Canada, they may also be proposed as international standards.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.971
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0400.010

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 head0.053
GPT teacher head0.268
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2008
Admission routes2
Has abstractyes

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