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Record W2916750939 · doi:10.1002/ange.201809607

Array‐basierte Sensorik mit der “chemischen Nase” in der Diagnostik und Wirkstoffentdeckung

2018· article· de· W2916750939 on OpenAlex
Yingying Geng, William J. Peveler, Vincent M. Rotello

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 · 2018
Typearticle
Languagede
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of British Columbia
FundersNational Institutes of HealthStockbridge School of Agriculture, University of Massachusetts Amherst
KeywordsPhysicsChemistryMolecular biologyBiology

Abstract

fetched live from OpenAlex

Abstract Array‐basierte Sensorplattformen – “Chemische Nasen/Zungen” – sind inspiriert vom olfaktorischen System von Säugetieren. Multiple Sensorelemente in diesen Geräten interagieren selektiv mit Zielanalyten und produzieren dabei ein spezifisches Antwortmuster, wodurch sie die Identifikation von Analyten ermöglichen. Dieser Ansatz bietet einzigartige Möglichkeiten im Vergleich mit “traditionellen”, hoch‐spezifischen Sensorelementen, z. B. Antikörpern. Array‐basierte Sensoren sind herausragend, wenn es um die Identifikation kleiner Veränderungen in komplexen Mischungen geht, und ebendiese Fähigkeit wird nun in der chemischen Biologie und klinischen Pathologie genutzt, ermöglicht durch ein vielfältiges Instrumentarium neuer Molekular‐, Biokonjugat‐ und Nanomaterial‐Techniken. Innovationen beim Design und der Analyse der Arrays stellen ein robustes Instrumentarium für moderne biomedizinische Zielsetzungen, einschließlich der Präzisionsmedizin, zur Verfügung.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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