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Record W2077707340 · doi:10.1109/auv.2014.7054413

Working towards seafloor and underwater iceberg mapping with a Slocum glider

2014· article· en· W2077707340 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSonarGliderUnderwater gliderIcebergMarine engineeringSeafloor spreadingSynthetic aperture sonarUnderwaterGeologyDroneSeabedTrajectorySea trialAcousticsRemote sensingEngineeringOceanography

Abstract

fetched live from OpenAlex

This paper reports on the integration and evaluation of a Tritech Micron mechanical scanning sonar into a Slocum underwater glider. The intend is to use the Slocum glider with the scanning sonar, to conduct seafloor and iceberg mapping tasks. The mechanical scanning sonar is installed in the extended, free flooded area of nose of the glider. After the successful integration, initial field trials were conducted in order to evaluate the performance in both seafloor surveying, and iceberg mapping modes. To achieve optimal performance, tuning of sonar parameters and vehicle trajectory control becomes significant. The performance of the vehicle and sonar are investigated in the field. Due to the transmission power absorbed by the extended nose cone, backscatter intensity is reduced, and receiver gain had to be increased, when compared to uncovered operations. With the experience gained from the initial field trial, areal surveys and autonomous iceberg mapping missions will be conducted in the future.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.376

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)0.0000.000

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 head0.016
GPT teacher head0.188
Teacher spread0.171 · 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