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Record W1490302697 · doi:10.1109/icma.2015.7237680

Development of a unique grip and lift mechanism for automated test water systems

2015· article· en· W1490302697 on OpenAlexafffund
Kevin Tai, Abdulrahman M. El‐Sayed, Mohammad Biglarbegian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Guelph
FundersMitacs
KeywordsLift (data mining)Leverage (statistics)BottleComputer scienceMechanism (biology)MachiningSimulationAutomotive engineeringEmbedded systemMechanical engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we develop a unique, two-pronged forklift mechanism as a part of a robotic system in an automated water-test sampler. This design consists of a leveraged forklift which can hold and lift caps from an array of sample bottles at one time. Unlike other methods already in the market, this design does not require extra active manipulation components. It also provides a downward force on the bottle while removing the cap ensuring the bottle and cap assembly will separate effectively. This design is also simple, cost effective, lightweight, and strong. We also optimized the dimensions of the forklift to provide the most mechanical leverage, and to decrease the amount of machining steps. We also analysed the proposed solution in terms of stress and found out that this design is resilient due to a safety factor of 3.37.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.701
Threshold uncertainty score0.158

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 head0.040
GPT teacher head0.238
Teacher spread0.198 · 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 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

Citations0
Published2015
Admission routes2
Has abstractyes

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