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Record W2158861107 · doi:10.1109/igarss.2005.1526346

Operational use of ship detection to combat illegal fishing in the Southern Indian Ocean

2005· article· en· W2158861107 on OpenAlexaboutno aff
M. Losekoot, P. Schwab

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsnot available
FundersCentre National d’Etudes Spatiales
KeywordsFishingEnforcementMaritime boundaryGeographyFisheryOceanographyRemote sensingEnvironmental scienceMeteorologyGeologyPolitical scienceInternational law

Abstract

fetched live from OpenAlex

The Kerguelen plateau in the Southern Indian Ocean is home to the highly sought-after Patagonian Toothfish, Dissostichus eleginoides, or Chilean Sea Bass. The efficient enforcement of fishing quotas and the repression of illegal fishing activities within the French and Australian exclusive economic zones represents a significant challenge to maritime authorities due to the size and remoteness of the area. Synthetic Aperture Radar (SAR) satellites are used to detect illegal vessels, thereby allowing patrol vessels to intercept them in a much more efficient and timely manner. The SENTRY transportable groundstation from IOSAT Inc. of Halifax was upgraded and deployed on Kerguelen Island where it autonomously acquires, processes and analyses images from the Radarsat-1 and Envisat satellites. Four times per day the station automatically produces ship reports less than two hours after each pass. The ship reports are combined with Argos positions from legal vessels in order to locate illegal vessels and direct patrol vessels. The station has been in successful operation for over a year and has demonstrably contributed to the repression of illegal fishing 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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.213
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations5
Published2005
Admission routes1
Has abstractyes

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