MétaCan
Menu
Back to cohort
Record W2897924502 · doi:10.1002/anie.201809607

Array‐based “Chemical Nose” Sensing in Diagnostics and Drug Discovery

2018· review· en· W2897924502 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

VenueAngewandte Chemie International Edition · 2018
Typereview
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of British Columbia
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesStockbridge School of Agriculture, University of Massachusetts Amherst
KeywordsElectronic noseComputer scienceAnalyteDrug discoveryIdentification (biology)Sensor arrayNanotechnologyChemical sensorSet (abstract data type)Computational biologyHuman–computer interactionArtificial intelligenceBioinformaticsMachine learningBiologyMaterials scienceChemistry

Abstract

fetched live from OpenAlex

Array-based sensor "chemical nose/tongue" platforms are inspired by the mammalian olfactory system. Multiple sensor elements in these devices selectively interact with target analytes, producing a distinct pattern of response and enabling analyte identification. This approach offers unique opportunities relative to "traditional" highly specific sensor elements such as antibodies. Array-based sensors excel at distinguishing small changes in complex mixtures, and this capability is being leveraged for chemical biology studies and clinical pathology, enabled by a diverse toolkit of new molecular, bioconjugate and nanomaterial technologies. Innovation in the design and analysis of arrays provides a robust set of tools for advancing biomedical goals, including precision medicine.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.813
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.019
GPT teacher head0.276
Teacher spread0.256 · 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