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Record W2136235758 · doi:10.1109/coase.2005.1506763

A scalable robotic-based laboratory automation system for medium-sized biotechnology laboratories

2005· article· en· W2136235758 on OpenAlexaff
Peyman Najmabadi, A.A. Goldenberg, Andrew Emili

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorkcellAutomationLaboratory automationThroughputScalabilityFlexibility (engineering)Embedded systemComputer scienceProtocol (science)FootprintTestbedWorkspaceInstrumentation (computer programming)RobotComputer hardwareEngineeringOperating systemArtificial intelligenceComputer networkMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents a new approach to laboratory automation integration with applications in automatic execution of various biotechnology (genomics and proteomics) protocols. The new configuration is called "tower-based configuration". It provides a scalable workcell that also includes instrumentation for sub-processes such as vortexing, shaking, incubation etc. Tower-based automation is a robotic-based configuration that allows improving the throughput of automated system without changing the robotic workspace or increasing the footprint of the system. Tower configuration consists of two arms mounted on a common cylindrical base, and is surrounded by stackers that carry laboratory processing instruments, labwares and other accessories. The requirement of high throughput is satisfied by parallel processing. A conceptual tower-based automated system for magnetic isolation of TAP tagged protein complexes protocol is presented and compared with a traditional automated system. Our performance investigation showed that the tower-based configuration has a high throughput to footprint ratio, high scalability, and wide protocol flexibility in comparison with traditional laboratory automation approaches.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.442

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 head0.005
GPT teacher head0.201
Teacher spread0.195 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

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