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Record W4210863549 · doi:10.1080/1088937x.2022.2032447

Arctic supply chain reliability in Baffin Bay and Greenland

2022· article· en· W4210863549 on OpenAlexaboutno aff
Jacob Taarup‐Esbensen, Ove Tobias Gudmestad

Bibliographic record

VenuePolar Geography · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticSupply chainBayReliability (semiconductor)The arcticBusinessHazardEnvironmental resource managementEnvironmental scienceOceanographyGeologyEcologyMarketing

Abstract

fetched live from OpenAlex

Despite the obvious economic advantages of utilising supply chains across the northern routes, there are significant challenges to their reliability. Every year an increasing number of ships venture into the region to supply, extract or transit the most northern parts of the world. However, supply chain reliability has been a significant challenge for ship operators, despite technological and organisational innovations. This paper investigates the hazards that face Arctic supply chain reliability in the region surrounding Baffin Bay and Greenland as well as the technological and organisational developments that are adopted to mitigate them. A bow-tie approach is used to illustrate the challenges faced by the shipping industry. We conclude that increased traffic will require significant investments in systems and infrastructure developments to manage Arctic hazards, thereby increasing reliability. Specifically, protective barriers like emergency response and icebreaker capacity need to be upgraded and positioned closer to emerging shipping lanes. Northwest Canada and Greenland are both poorly covered in terms of helicopter search and rescue and icebreaker availability. The consequence is that, with the increase in traffic outside the traditional busy routes in the south, supply chains lack access to effective Arctic hazard barriers.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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.

Study designObservational
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

Citations10
Published2022
Admission routes1
Has abstractyes

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