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Record W2071080671 · doi:10.1115/omae2014-23085

Backbone for Escape, Evacuation and Rescue From Arctic Facilities: A Systematic Approach

2014· article· en· W2071080671 on OpenAlexaboutno aff
Mark Longrée, Sven Hoog

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
FundersEuropean Commission
KeywordsArcticHuman settlementContext (archaeology)The arcticBusinessOrder (exchange)Environmental planningEnvironmental resource managementGeographyEnvironmental scienceFinance

Abstract

fetched live from OpenAlex

In turn of the global warming and driven by the constant need for resources an increasing number of commercial and scientific activities conquer the Arctic in order to benefit from almost untouched resources like oil and gas but also from the overwhelming nature. These activities are accompanied by a steadily increasing number of vessels transporting goods but also operating personnel, scientists or tourists. Especially the number of tourists visiting the Arctic can reach far more than 1000 per vessel, resulting in growing headaches for the responsible safety and security authorities in the Arctic surrounding countries. Up to now no suitable Escape, Evacuation and Rescue (EER) concept is in place to cope with these challenges when it comes to hazardous situations. In this context IMPaC ([1]) developed a new and appropriate EER concept for the Arctic, exceeding the currently dominant small and isolated settlements along the coastlines in Denmark (Greenland), Norway, Russia, Canada and the US. One question seems to be central: Is there any requirement and benefit beyond the currently used small rescue station? Yes, we strongly believe that there is a growing demand for suitable infrastructure coming from various industries. Beyond rescue objectives there is a demand for people working and living in this area all year long, for a few days, weeks or months using these settlements for their specific needs. This led us to the idea of the provision of a common-use infrastructure for multiple industries. The commonly used infrastructure maximizes the use of the remote and very expensive infrastructure and minimizes the impact on the environment in this part of the world. Potential users of this infrastructure would be: • Oil & Gas Industry, driven by the increased world energy demand • Marine Transport & Tourism Industry, driven by declined arctic ice and new sea routes via the Arctic sea • Fishery Industry • Scientific community Any EER concept for the Arctic has to cope with several specific environmental and spatial challenges as addressed by the EU joint research project ACCESS ([2]), where IMPaC participates. The paper introduces the new EER concept and focuses especially on its beneficial, efficient and safe operability in the Arctic recording an increasing number of commercial and scientific activities.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0030.002
Scholarly communication0.0070.008
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.006

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 head0.009
GPT teacher head0.172
Teacher spread0.164 · 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
GenreMethods

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

Citations5
Published2014
Admission routes1
Has abstractyes

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