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Record W182967772 · doi:10.1007/978-1-60327-567-5_6

Label-Free Detection with the Resonant Mirror Biosensor

2009· review· en· W182967772 on OpenAlexaff
Mohammed Zourob, Souna Elwary, Xudong Fan, Stephan Mohr, N J Goddard

Bibliographic record

VenueMethods in molecular biology · 2009
Typereview
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsBioPhage Pharma (Canada)
Fundersnot available
KeywordsBiosensorEvanescent waveAnalyteNanotechnologyInstrumentation (computer programming)WaveguideRefractive indexMaterials scienceChemistryOptoelectronicsComputer scienceChromatography

Abstract

fetched live from OpenAlex

The resonant mirror (RM) biosensor is a leaky waveguide-based instrument that uses the evanescent field to probe changes in the refractive index at the sensing surface.The RM can therefore be used to monitor in real-time and label-free the interaction between an analyte in solution and its biospecific partner immobilized on the waveguide surface.The RM has been used in studying the interaction of a variety of moieties including proteins, carbohydrates, cells, nucleic acids and receptors, leading to applications in areas such as clinical diagnostics, homeland security, and pharmaceutical and biomolecular interactions. This chapter will review the principle of this biosensor, and the recent advances in instrumentation, different immobilization chemistries, and kinetic studies, as well as some applications.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.095
GPT teacher head0.494
Teacher spread0.400 · 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
GenreReview

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

Citations18
Published2009
Admission routes1
Has abstractyes

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