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Record W6980088287

Avoiding the next Titanic: Are we ready for a major maritime incident in the Arctic?

2010· article· en· W6980088287 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2010
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsCruiseCrewArcticSearch and rescueThe arcticMaritime safetyOpen waterExtreme ColdFlexibility (engineering)
DOInot available

Abstract

fetched live from OpenAlex

The Arctic is open to major passenger activy with cruise ships carrying 1.5 million passengers into remote polar waters this year. These ships rely on loose guidelines and out-dated navigation charts to expand their territory; the potential for a major incident is foretold by three vessel groundings in the Northwest Passage this past season. Extreme distances between search-and-rescue resources and cruise operations limit the current emergency response; less than 2% of Canadian distress calls come from north of 60° latitude and the anticipated response time is 5 or more days. Passengers are not prepared for surviving the elements in the lag between evacuation from a ship and rescue by current crew training and life saving equipment that are not rated for thermal protection. Research into long-duration cold exposure, developments in thermal protection and changes to regulations are among the steps needed to improve the odds of surviving a major maritime incident in the Arctic. The complexity of a ship and its interactions with emergency responders call for a system-of-systems approach. A multi-disciplinary, international group representing some of the stakeholders have completed some research that highlights some of the potential risks in evacuating to lifeboats or life rafts in the North and proposes solutions. Changes to practice, revised regulations and new technologies tailored for the Arctic are emerging. More international coordination and collaboration on marine safety research is needed.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.259
Teacher spread0.229 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2010
Admission routes2
Has abstractyes

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