MétaCan
Menu
Back to cohort

Research on Automatically Generating Method for Three-Dimension Virtual Model of Buoy Based on S-57 Chart Data

2011· article· en· W2035293404 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsnot available
FundersInstitute of Musculoskeletal Health and ArthritisJimei UniversityNatural Science Foundation of Fujian ProvinceNational Natural Science Foundation of China
KeywordsBuoyChartDimension (graph theory)Computer scienceEngineeringArtificial intelligenceSimulationEngineering drawingComputer visionComputer graphics (images)Marine engineeringMathematics

Abstract

fetched live from OpenAlex

With appearance of variety kinds of three-dimension buoy application system, the design and production of three-dimension virtual model of buoy is increasingly becoming an important and tedious work. Artificial modeling way adopted in the past is inefficient, and the light quality is difficult to achieve. The paper puts forward automatically generating method for three-dimension virtual model of buoy based on S-57 chart data (AGM for short), and describes extraction and analysis of buoy data from S-57 chart data, automatically generation of three-dimension buoy model and the flashing animation of buoy light quality in detail, and finally gives the experimental data and 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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.493
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
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 head0.429
GPT teacher head0.489
Teacher spread0.060 · 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