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Record W3025359443 · doi:10.1139/facets-2019-0067

Analysis of heavy fuel oil use by ships operating in Canadian Arctic waters from 2010 to 2018

2020· article· en· W3025359443 on OpenAlexaffvenueabout
Nicolien van Luijk, Jackie Dawson, Alison Cook

Bibliographic record

VenueFACETS · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsArcticEnvironmental scienceBayOil spillThe arcticInletOceanographyScope (computer science)Marine engineeringGeographyEnvironmental protectionEngineeringGeologyComputer science

Abstract

fetched live from OpenAlex

In 2018, The International Maritime Organization, officially proposed consideration of a ban on heavy fuel oil (HFO) use by ships in the Arctic, because of the widely accepted understanding that HFO presents a threat to the marine environment. There is currently a lack of understanding of the scale and scope of HFO use by ships operating in Canadian Arctic waters, thus it is difficult to comprehensively evaluate the effect that such a ban may have in mitigating risk from HFO use. In this study, we conducted a spatial analysis of HFO use among ships operating in Canadian Arctic waters between 2010 and 2018. Our findings show that approximately 37% of the total number of ships that have travelled through the Canadian Arctic between 2010 and 2018 use HFO, and nearly all of these ships fall within three vessel categories: general cargo, bulk carriers, and tanker ships. In addition, HFO-fueled ships made up approximately 45% of the total distance (kilometres) travelled by all vessels between 2010 and 2018. The data also show that the majority of HFO use occurs in certain geographic areas, such as Baffin Bay near Pond Inlet and the Hudson Strait.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.053
GPT teacher head0.301
Teacher spread0.248 · 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 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

Citations15
Published2020
Admission routes3
Has abstractyes

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