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Record W2010249150 · doi:10.1109/oceans.2010.5664082

Solutions for practice-oriented requirements for optimal path planning for the AUV “SLOCUM Glider”

2010· preprint· en· W2010249150 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaDeutsche Forschungsgemeinschaft
KeywordsGliderPath (computing)Computer scienceEngineeringMarine engineeringComputer network

Abstract

fetched live from OpenAlex

This paper presents a few important practice-oriented requirements for optimal path planning for the AUV “SLOCUM Glider” as well as solutions using fast graph based algorithms. These algorithms build upon the TVE (time-varying environment) search algorithm. The experience with this algorithm, requirements of real missions along the Newfoundland and Labrador Shelf and the idea to find the optimal departure time are the motivation to address the field of research, which is described in this paper. The main focus of this paper is a discussion of possible methods to accelerate the path planning algorithm, without deterioration of the results.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0040.003
Research integrity0.0010.001
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 head0.147
GPT teacher head0.387
Teacher spread0.240 · 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

Quick stats

Citations29
Published2010
Admission routes3
Has abstractyes

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