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Record W4392247257 · doi:10.1016/j.ohx.2024.e00516

BioCloneBot: A versatile, low-cost, and open-source automated liquid handler

2024· article· en· W4392247257 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHardwareX · 2024
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsOpen sourceComputer scienceEmbedded systemOperating systemSoftware

Abstract

fetched live from OpenAlex

Liquid handler systems can provide significant benefits to researchers by automating laboratory work, however, their unaffordable price provides a steep barrier to entry. Therefore, we provide the BioCloneBot, a versatile, low-cost, and open-source automated liquid handler. This system can be easily built with 3D-printed parts and readily available commercial components. The BioCloneBot is highly adaptive to user needs and facilitates various liquid handling tasks in research and diagnostics. Its user-friendly interface and programmable nature make it suitable for a wide range of applications, from small-scale experiments to larger laboratory setups. By utilizing BioCloneBot, researchers and scientists can streamline their liquid handling processes without the financial constraints posed by traditional systems. In this paper, we detail the design, construction, and validation of BioCloneBot, showcasing its precise control, accuracy, and repeatability in various liquid handling tasks. The open-source nature of the system encourages collaboration and customization, enabling researchers to contribute and adapt the technology to specific experimental 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.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.691
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.259
Teacher spread0.245 · 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