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Record W2120966636 · doi:10.1016/j.jala.2006.05.010

Liquid-Handling Technology and the Method of Electrostatic Drop Transfer to Improve Dispensing Performance

2006· article· en· W2120966636 on OpenAlexaff
Nahid N. Jetha, Andre Marziali

Bibliographic record

VenueJALA Journal of the Association for Laboratory Automation · 2006
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSyringeProcess engineeringDrop (telecommunication)ThroughputSample (material)Computer scienceTransfer (computing)NanotechnologyMaterials scienceSimulationEngineeringMechanical engineeringChromatographyChemistryTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

Liquid handling is a critical component of highly parallel processes such as high-throughput screening (HTS) and genomic analysis. Such processes require the use of smaller sample volumes and the ability to dispense without contact, driving the development of a variety of liquid-handling technologies to meet these needs. Such technologies have associated advantages and disadvantages, which makes choice of the right system application dependent. Syringe technology remains the most cost effective and versatile. It suffers, however, from the inability to dispense submicroliter volumes without contact. We provide a brief overview of liquid-handling technology, and present a new method of sample transfer based on electrostatic forces (Jetha, N. N.; Marziali, A. Electrostatic device for active transfer of submicroliter samples from syringe pipettors. BioTechniques. 2006, 40, 148–151) that can be incorporated into liquid-handling systems, enabling highly accurate and repeatable non-contact dispensing of submicroliter volumes. (JALA 2006;11:278–80)

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)0.002
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 score0.429
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.218
Teacher spread0.215 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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