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Record W4238379293 · doi:10.1016/s1535-5535-04-00277-1

Setting up High-Throughput Low-Volume Sequencing and PCR Reactions Using an Automated System Equipped with Precision Glass Syringes and a Non-Contact Microsolenoid Dispenser

2003· article· en· W4238379293 on OpenAlexaff
Hui‐Chung Wu, A. McIntyre James, Nelson Braunthal, Jean Shieh, Arezou Azarani

Bibliographic record

VenueJALA Journal of the Association for Laboratory Automation · 2003
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsPhenomenome Discoveries (Canada)
Fundersnot available
KeywordsThroughputVolume (thermodynamics)SyringeSample (material)Channel (broadcasting)Biomedical engineeringComputer scienceComputer hardwareMaterials scienceChromatographyChemistryEngineeringTelecommunicationsMechanical engineeringPhysicsWireless

Abstract

fetched live from OpenAlex

The advantages of using a new automated system, the Hydra-Plus-One System equipped with 96 or 384 precision glass syringes and a non-contact microsolenoid dispenser, in setting up high-throughput low-volume sequencing reactions and PCR are described. Using the syringe-based dispenser, which is the Hydra-PP part of this system, wet dispenses of as small as 100 nL with CVs of less than 10% can be accomplished. The single-channel, non-contact microsolenoid dispenser part of the system can dispense samples as low as 100 nL (with CVs of less than 10%) at a speed of 58s per 96 dispenses into any plate format (SBS footprint). The advantages associated with the use of the Hydra-Plus-One System for setting up PCR and sequencing reactions are high precision at nanoliter-dispense range; speed; and minimal waste of precious and expensive samples. The single-channel dispenser eliminates the dead volume associated with aspirating from reservoirs or troughs and thereby reduces sample waste. In addition, virtually all material can be recovered from the dispenser. Finally, non-contact dispensing enables distribution of sample into wells without any in-between-dispenses washing requirements.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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