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Record W2141702479 · doi:10.1109/robot.1997.620008

An eye-hand system for automated paper recycling

2002· article· en· W2141702479 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
FieldComputer Science
TopicImage and Object Detection Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer vision

Abstract

fetched live from OpenAlex

Presents a robotic system used for detection, sorting and grading of paper objects as a part of an automated paper recycling system. The system uses stereo vision to estimate in real time the spatial position of moving objects on a conveyor to be picked by the robot arm. It was proved in the literature that stereo vision may be successfully used to estimate position of moving objects, with a priori known shape, and generate commands to a robot arm for picking up the object. A special computing architecture is introduced for performing the task in real time. A vector of geometric and textural features is used for sorting the paper, according to its grade. The grading process uses color vision and additional contact ultrasonic sensors. A new data fusion paradigm, based on supervised learning, is used. The data fusion algorithm combines stereo vision and ultrasonic sensors to detect and grade paper objects.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.259

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 communication0.0000.001
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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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

Citations27
Published2002
Admission routes1
Has abstractyes

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