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Record W2983971137

The Memorial Explorer: Developing the role of AUVs in under-ice research

2010· article· en· W2983971137 on OpenAlexaboutno aff
Peter King, Ron Lewis, Dan Walker, P Morgan Alexander, Neil Bose, A. P. Worby

Bibliographic record

VenueeCite Digital Repository (University of Tasmania) · 2010
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsnot available
Fundersnot available
KeywordsSuiteSoftware deploymentSea iceUnderwaterArcticOceanographyPhase (matter)The arcticAeronauticsEngineeringRemote sensingMeteorologyComputer scienceGeologyGeographyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Autonomous Underwater Vehicles are a leading technology for under-ice deployment and research. Memorial University and its Explorer AUV, in cooperation with the University of Tasmania and the Australian Antarctic Division, has embarked on a multi-phase development program that will lead to a scientific mission in the Australian Antarctic. Completion of Phase I saw the AUV deployed in the Canadian high Arctic and acoustic data collection performed in the Australian Antarctic. Currently in Phase II, the AUV is being fitted with a full suite of survey tools and will see developments in the fields of navigation, positioning and mission completion. Phase III will see further developments in AUV technology for under-ice research, with the ultimate goal being the AUV deployed in the Antarctic for a scientific mission to explore the role of sea-ice in the World's climate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.217
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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 routes1
Has abstractyes

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Same venueeCite Digital Repository (University of Tasmania)Same topicUnderwater Vehicles and Communication SystemsFrench-language works237,207