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Record W2416380766 · doi:10.1002/fee.1293

Cutting a lifeline to maritime crime: marine insurance and <scp>IUU</scp> fishing

2016· review· en· W2416380766 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2016
Typereview
Languageen
FieldSocial Sciences
TopicMaritime Security and History
Canadian institutionsInStream Fisheries Research (Canada)University of British Columbia
FundersWaterloo Foundation
KeywordsFishingBusinessFishery

Abstract

fetched live from OpenAlex

Restricting or eliminating access to insurance for illegal, unreported, and unregulated ( IUU ) fishing vessels could alter the associated balance of costs and benefits in favor of reducing IUU fishing activity. We present a simplified conceptual economic model for IUU fishing that demonstrates how having marine insurance is financially beneficial, thereby encouraging IUU fishing. By analyzing available data on vessels and their affiliations to insurers, we also determined that some IUU vessels are covered by insurance. IUU fishing is not an issue that is currently being addressed by insurers, and an opportunity exists for developing strategies to confront this global problem within the marine insurance sector. We believe that restricted access can be achieved through modified policies and procedures, and recommend that – at a minimum – insurers consult officially verified IUU vessel lists to make certain that listed vessels are not granted insurance coverage.

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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