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SAR, AIS, and Contextual Data for the Identification of Source Mechanisms for Ship Pollution False Positives

2022· article· en· W4312808900 on OpenAlexaboutno aff
Gordon Staples

Bibliographic record

VenueIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsWakeEnvironmental scienceOceanographyPlanktonMarine pollutionMeteorologyRemote sensingAutomatic Identification SystemPollutionGeologyComputer scienceGeographyEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

On September 2,2015, a RADARSAT-2 ScanSAR Narrow image was acquired of the Juan de Fuca Strait and the Strait of Georgia, British Colombia, Canada as part of routine ocean-pollution surveillance. The vessel, Coastal Intrigue, and a linear, slick-like feature trailing the stern of the vessel were detected. The source of the CIF was attributed to three possible mechanisms, namely a plankton bloom, turbulent wake, or some type of discharge from the Coastal Intrigue. At the time of the RADARSAT-2 acquisition, there were no reported plankton blooms in the Juan de Fuca Strait or the Strait of Georgia, so a plankton bloom was eliminated as a mechanism. A turbulent wake was also eliminated because turbulent wakes are seldom detected in the area where the Coastal Intrigue was travelling and the vessel Eva, which was travelling on essentially a parallel course as the Coastal Intrigue, did not produce a SAR-detected wake. Therefore, the most likely source-mechanism for the CIF was discharge (either oil or similar) from the Coastal Intrigue. The analysis of the RADARSAT-2 image acquired on September 2, 2015, AIS data, and wind data supports the conclusion that the CIF was from the Coastal Intrigue. There was, however, not enough information available to definitively identify the CIF substance other than the statement that the most likely source was either bilge oil or some other type of substance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.268
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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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