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Record W2069514222 · doi:10.1115/imece2006-15165

Optimal Architecture of Shack Hartmann Wave-Front Sensor for Microfluidic Applications

2006· article· en· W2069514222 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

VenueFluids Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsMicroscale chemistryMicrofluidicsMicrosystemMicroelectromechanical systemsSeedingComputer scienceMaterials scienceAcousticsNanotechnologyEngineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

Means of measuring temperature and fluid flow in microelectromechanical systems (MEMS) continue to show limitations. This paper discusses the development of a noninvasive optical based temperature mapping technique for use in microsystems. The technique employs the Shack-Hartmann wave-front sensor (SHWFS), with documented accuracy in macroscale applications of ±0.7°C [1]. Microscale models indicate the potential to collect data with the same accuracy. With continued development, fluid flow monitoring by thermally seeding an element of fluid and using the SHWFS to detect the location of this heated element will be possible. This measurement technique can be applied to a variety of microfluidic devices, including biomedical devices, since the temperature "seed" can be small enough to prevent damage to sensitive biological systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.500
Threshold uncertainty score0.830

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 head0.009
GPT teacher head0.203
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