MétaCan
Menu
Back to cohort
Record W2460351912

Side Scan Sonar for Hydrography - An Evaluation by the Canadian Hydrographic Service

2015· article· en· W2460351912 on OpenAlexaboutno aff
Rod Bryant

Bibliographic record

VenueThe International Hydrographic Review · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
Fundersnot available
KeywordsHydrographic surveySonarSide-scan sonarDepth soundingHydrographyRemote sensingComputer scienceEngineeringGeographyCartographyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Hydrographic surveys have improved in accuracy and efficiency over the last few decades with advances in electronics and data processing. Electronic positioning systems with automatic data loggers now make it possible to survey accurately at greater speed. Improved data processing systems eliminate the time-consuming, laborious task of scaling and plotting. The modern surveyor, however, is still plagued with the lack of knowledge of what lies between his sounding lines. Sonar developments promise to improve this situation as commercial equipment becomes available. Omnidirectional scanning sonars can view large areas of the bottom and display the features on a CRT display; searchlight type sonars yield range, azimuth and depression angle with the potential of making depth measurements far removed from the survey vessel; multiple beam sonars simultaneously sound sectors along the vessel path and side-looking sonars delineate features of the bottom on wide swaths, either side of the survey craft. This paper deals with the latter type, the dual side-scan sonar, specifically the type produced by E.G. & G. and Klein Associates of the United States. The principles of operation are presented, the results of an evaluation are given, and the use of the sonar over a field survey season is outlined.

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)0.006
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.102
GPT teacher head0.331
Teacher spread0.230 · 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 designOther design
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

Citations10
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe International Hydrographic ReviewSame topicUnderwater Acoustics ResearchFrench-language works237,207