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Record W4283120537 · doi:10.3390/biology11060932

The Effects of Silicone Enclosure Colour on the Function of Optical Sensors

2022· article· en· W4283120537 on OpenAlexafffund
Garrett Frank, Shahbaz Askari, Katharina Raschdorf, Sadra Khosravi, Brian K. Kwon, Babak Shadgan

Bibliographic record

VenueBiology · 2022
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersDefense Advanced Research Projects AgencyMichael Smith Health Research BC
KeywordsPhotodetectorSiliconeWavelengthMaterials scienceOpticsEnclosureIntegrating spherePenetration (warfare)Optical powerReflectivityMagnitude (astronomy)Penetration depthOptoelectronicsBiologyPhysicsComposite materialAstrophysics

Abstract

fetched live from OpenAlex

The colour of the silicone enclosure of an implantable reflectance-based optical probe plays a critical role in sensor performance. Red-coloured probes that are highly reflective to near-infrared light have been found to increase photodetector power by a factor of 6 for wavelengths between 660 and 950 nm and triple the magnitude of measured cardiac pulsations compared to traditional black probes. The increase in photodetector power and cardiac pulsation magnitude is presumably due to increased spatial range resulting from a higher magnitude of superficial tissue scattering. Conversely, probes with highly absorbent colours such as black and blue result in more stable signals and are expected to have higher spatial resolution and depth of penetration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.280
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2022
Admission routes2
Has abstractyes

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