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Record W2108050352 · doi:10.1109/mwscas.2001.986250

Robotic mimicking control system

2002· article· en· W2108050352 on OpenAlexafffundabout
S. Arseneau, Rachel Nicholls, M. Farooq, Andrew Hopkinson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsRoyal Military College of Canada
FundersCanadian Space Agency
KeywordsComputer visionOrientation (vector space)Computer scienceArtificial intelligencePhotogrammetryRobotic armController (irrigation)Joint (building)Robotic handTeleroboticsRobotEngineeringMobile robot

Abstract

fetched live from OpenAlex

This research activity is being carried out between the Canadian Space Agency and the Royal Military College of Canada. The objective is to develop a Robotic Mimicking Control System (RMCS) to provide a more user friendly and efficient robotic controller. A video camera is used to acquire a still photograph of a model joint. The selected image is processed and a closed-form algorithm for photogrammetry is employed to determine the orientation of the model joint objects. This orientation information is passed on to the physical robotic arm. The commands are executed by the robotic arm, which enables it to imitate the orientation of the model joint.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.025
GPT teacher head0.171
Teacher spread0.146 · 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 designSimulation or modeling
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

Citations1
Published2002
Admission routes3
Has abstractyes

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