MétaCan
Menu
Back to cohort
Record W1963912627 · doi:10.1109/oceanse.2007.4302211

A Methodology to Assess Capabilities Against Underwater Targets in Harbour Protection

2007· article· en· W1963912627 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

VenueOCEANS 2007 - Europe · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsUnderwaterHarbourUnmanned underwater vehicleSonarMarine engineeringTrack (disk drive)EngineeringTracking (education)Computer scienceComputer securitySimulationArtificial intelligenceOceanographyGeology

Abstract

fetched live from OpenAlex

The paper describes a stochastic model of effectiveness for a harbour-defence system to counter an underwater threat. This model is applied to a scenario in harbour X. The defence consists of an active sonar with a detection and tracking system, an intercept platform with a non lethal weapon system, and an underwater barrier with trip-wire sensors. We showed that the effectiveness of the defence system against an UUV threat depends on the reaction-time of the interceptor (the time required to launch the interceptor after a track has been initiated). Short reaction-time is required for the sonar and the interceptor to be effective against the UUV threat. However, when the interceptor reaction-time is long, the underwater barrier system becomes the only effective system against the UUV. The paper also introduces a novel and efficient algorithm to determine the probability of track-initiation and presents the concept of a safety zone as well as determine its benefits.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.114
GPT teacher head0.315
Teacher spread0.201 · 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