MétaCan
Menu
Back to cohort
Record W2345074372 · doi:10.1364/boe.7.002078

Feature issue introduction: biophotonic materials and applications

2016· article· en· W2345074372 on OpenAlexaff
Kwang-Sup Lee, Chantal Andraud, Kaoru Tamada, Konstantin Sokolov, Kenneth T. Kotz, Gang Zheng

Bibliographic record

VenueBiomedical Optics Express · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiophotonicsContext (archaeology)NanotechnologyPhotonicsComputer scienceBiosensorMaterials scienceOptoelectronicsBiology

Abstract

fetched live from OpenAlex

Biophotonics can be defined as the interplay of light and biological matter. The percolation of new optical technology into the realm of biology has literally shed new light into the inner workings of biological systems. This has revealed new applications for optics in biology. In a parallel trend, biomolecules have been investigated for their optical applications. Materials are playing a central role in the development of biophotonics. New materials, fabrication methods, and structures are enabling new biosensors, contrast agents, imaging strategies, and assay methods. Similarly, biologic materials themselves can be used in photonic devices. In this context, two open-access, rapid-publication journals from The Optical Society of America (OSA), Optical Materials Express and Biomedical Optics Express, will publish a joint feature issue covering advances in biophotonics materials.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: none
Teacher disagreement score0.155
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1550.058

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 head0.020
GPT teacher head0.296
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueBiomedical Optics ExpressSame topicPhotoreceptor and optogenetics researchFrench-language works237,207