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Record W1975728156 · doi:10.1117/12.864698

Mapping AIS coverage for trusted surveillance

2010· article· en· W1975728156 on OpenAlexaffabout
Anna-Liesa S. Lapinski, Anthony W. Isenor

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsAutomatic Identification SystemComputer sciencePosition (finance)Computer securityCollision

Abstract

fetched live from OpenAlex

Automatic Identification System (AIS) is an unattended vessel reporting system developed for collision avoidance. Shipboard AIS equipment automatically broadcasts vessel positional data at regular intervals. The real-time position and identity data from a vessel is received by other vessels in the area thereby assisting with local navigation. As well, AIS broadcasts are beneficial to those concerned with coastal and harbour security. Land-based AIS receiving stations can also collect the AIS broadcasts. However, reception at the land station is dependent upon the ship's position relative to the receiving station. For AIS to be used as a trusted surveillance system, the characteristics of the AIS coverage area in the vicinity of the station (or stations) should be understood. This paper presents some results of a method being investigated at DRDC Atlantic (Canada) to map the AIS coverage characteristics of a dynamic AIS reception network. The method is shown to clearly distinguish AIS reception edges from those edges caused by vessel traffic patterns. The method can also be used to identify temporal changes in the coverage area, an important characteristic for local maritime security surveillance activities. Future research using the coverage estimate technique is also proposed to support surveillance 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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.

Study designBench or experimental
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

Citations2
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicMaritime Navigation and SafetyFrench-language works237,207