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Record W1970536964 · doi:10.1039/c0an01007c

Microcantilever biosensors for chemicals and bioorganisms

2011· review· en· W1970536964 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.

Bibliographic record

VenueThe Analyst · 2011
Typereview
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Alberta
FundersNational Institute of Neurological Disorders and Stroke
KeywordsBiosensorAnalyteNanotechnologyProtein detectionComponent (thermodynamics)ChemistryComputer scienceMaterials scienceChromatographyPhysics

Abstract

fetched live from OpenAlex

In the last fifteen years, microcantilevers (MCLs) have been emerging as a sensitive tool for the detection of chemicals and bioorganisms. Because of their small size, lightweight, and high surface-to-volume ratio, MCL-based sensors improve our capability to detect and identify biological agents by orders of magnitude. A biosensor is a device for the detection of an analyte that combines a biological component with a physicochemical detector component. The MCL biosensors have recently been reviewed in several papers. All of these papers were organized based on the sensing biological elements (antibody, enzyme, proteins, etc.) for recognition of analytes. In this review, we intend to summarize the microcantilever biosensors in a format of each specific chemical and bioorganism species to make information on individual biosensors easily accessible. We did this to aid researchers to locate relevant references.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.064
GPT teacher head0.323
Teacher spread0.259 · 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