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Intercept Considerations for Devising a Dipping Sonar Search Strategy to Locate an Approaching Submarine

2022· article· en· W4317792420 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

Venue2022 Winter Simulation Conference (WSC) · 2022
Typearticle
Languageen
FieldEngineering
TopicGuidance and Control Systems
Canadian institutionsCanadian Armed ForcesDefence Research and Development Canada
Fundersnot available
KeywordsSubmarineSonarMarine engineeringRange (aeronautics)Computer scienceNavyTrajectoryEngineeringGeologyAeronauticsAerospace engineeringArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

A hostile conventional submarine will attempt to get within close enough range to a ship so as to launch a torpedo. To counter this, an Anti-Submarine Warfare (ASW) helicopter with dipping sonar capability can be used to search for an approaching submarine. In situations where earlier contact information of the submarine by an external source is available, intercept trajectory considerations can be used to determine an intercept zone for the submarine from which it can attack the ship. This information can then be used to devise a search strategy for the helicopter to locate the submarine, after which counter measures can be taken against the submarine. This problem has been investigated using a naval combat modelling environment, with results of the methodology development, implementation and analysis reported here.

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)0.000
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: none
Teacher disagreement score0.654
Threshold uncertainty score0.999

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)0.0010.000

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 head0.108
GPT teacher head0.303
Teacher spread0.196 · 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