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Record W4299322923 · doi:10.5957/icetech-2010-167

Traffic and Casualty Trends in Canadian Arctic Shipping

2010· article· en· W4299322923 on OpenAlexaboutno aff
Brad Judson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticThe arcticSea iceEnvironmental scienceEnvironmental resource managementBusinessTransport engineeringEnvironmental planningGeographyMeteorologyOceanographyEngineering

Abstract

fetched live from OpenAlex

Significant research on Arctic sea ice trends and the potential for resource development have been well documented and illustrated as drivers for changes to Arctic shipping traffic patterns. There is a strong awareness of the potential risks to the environment such as an oil spill in ice as well as impacts on traditional human activity. Similarly, there is awareness that there will be a demand for increased navigation services such as aids to navigation, charting and emergency response capacity. However, many questions remain about what impact sea ice trends and resource development have had on shipping and accidents. To date, the Arctic Marine Shipping Assessment (AMSA) project has provided a snapshot of Arctic shipping traffic patterns and activity for the year 2004 and suggests a further research opportunity exists to conduct a trend analysis of shipping activity. The AMSA report suggests that “As marine activity continues to expand in the Arctic, statistical trends indicate that the potential risk of vessel mishaps and marine pollution incidents also increases” (Arctic Council, 2009). However, this is not necessarily the case where risks are managed. Accident trends in the Canadian Arctic suggest that safety management, vessel design and navigation experience have had positive impacts and one must look more closely at specific areas of operation, vessel types and activity to identify opportunities to improve risk management including both prevention and response. So the question remains “What can we learn from recent trends in vessel traffic and accident rates to better understand potential navigation impacts in the future?” Using the AMSA Shipping Database (Arctic Council, 2009) and a spatial trend analysis of Canadian Arctic shipping traffic and vessel accident rates covering the period 1987 to 2008, this paper will report on preliminary findings, show where accident rates are increasing and decreasing, provide traffic trends for each Shipping Safety Control Zone, help to dispel a few myths, and possibly confirm other rumours.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.011
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.028
GPT teacher head0.329
Teacher spread0.301 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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