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Record W2106842624 · doi:10.1109/aim.1997.652870

An innovative machine for automated cutting of fish

2002· article· en· W2106842624 on OpenAlexaff
C.W. de Silva, N. Wickramarachchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsServomotorConveyor beltFish <Actinopterygii>Computer scienceMotion controlEngineeringMechanical engineeringRobotArtificial intelligence

Abstract

fetched live from OpenAlex

Summary form only given. This paper presents an innovative machine for fish cutting. The new machine does not possess many of the shortcomings of the Iron Butcher which is commonly used in the industry for head removal of fish. The machine uses a double-bladed rotary cutter, arranged in a V-configuration and driven by 3-phase induction motors, in order to improve the recovery of fish meat. Also, two servomotors have been used for positioning of the cutter with respect to a fish. An imaging system senses each fish and generates the desired cutting location, and positions the cutter accordingly. An ultrasonic sensor senses the thickness of each fish, and positions a delivery platform using this information. In addition to sensing the cutter loads, conveyor motion, and the performance of the servo systems, a secondary imaging system has been incorporated at the product-exit end of the machine to determine the cutting quality. This information is preprocessed on line and used in a knowledge-based supervisory control system, which is able to make the necessary adjustments in the machine if the performance is unacceptable.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.433

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 communication0.0000.000
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 head0.045
GPT teacher head0.294
Teacher spread0.249 · 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 teacher head, 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

Citations0
Published2002
Admission routes1
Has abstractyes

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