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Record W2071571360 · doi:10.1039/c5lc00234f

Micromilling: a method for ultra-rapid prototyping of plastic microfluidic devices

2015· review· en· W2071571360 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

VenueLab on a Chip · 2015
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
FundersNational Human Genome Research InstituteNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Cancer InstituteBill and Melinda Gates Foundation
KeywordsRapid prototypingMicrofluidicsMaterials scienceNanotechnologyProcess engineeringComputer scienceEmbedded systemEngineeringComposite material

Abstract

fetched live from OpenAlex

This tutorial review offers protocols, tips, insight, and considerations for practitioners interested in using micromilling to create microfluidic devices. The objective is to provide a potential user with information to guide them on whether micromilling would fill a specific need within their overall fabrication strategy. Comparisons are made between micromilling and other common fabrication methods for plastics in terms of technical capabilities and cost. The main discussion focuses on "how-to" aspects of micromilling, to enable a user to select proper equipment and tools, and obtain usable microfluidic parts with minimal start-up time and effort. The supplementary information provides more extensive discussion on CNC mill setup, alignment, and programming. We aim to reach an audience with minimal prior experience in milling, but with strong interests in fabrication of microfluidic devices.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.386
Teacher spread0.293 · 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