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Record W2040849793 · doi:10.4236/jbise.2015.82011

Modern Probe-Assisted Methods for the Specific Detection of Bacteria

2015· article· en· W2040849793 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

VenueJournal of Biomedical Science and Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsConcordia University
Fundersnot available
KeywordsBiochemical engineeringNanotechnologyComputer scienceHuman healthBacteriaBiosensorPathogenic bacteriaMaterials scienceBiologyEngineeringMedicine

Abstract

fetched live from OpenAlex

This review intends to present an overview of methods currently under development for the specific and sensitive detection of pathogenic bacteria that exist in a variety of human environments. Bacteria continue to be a major health threat in general, and much effort is being deployed to counteract this problem. In a first instance, current and efficient techniques in use for the detection of bacteria are described. In a second instance, this review serves to compare the more conventional techniques to emerging technologies for the direct (non-labelled) detection of bacteria (referred to as “biosensors”). These approaches are mainly optical, piezoelectric, and electro-chemical in nature. They are cost-effective, quite sensitive, and potentially portable for rapid on-site/real-time detection, and rapid prevention. These devices are comprised of specific chemical/ biochemical probes immobilized onto physical transducers. This work also presents comparisons between the efficiencies (assay time and sensitivity) of various techniques being employed.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.141

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.030
GPT teacher head0.279
Teacher spread0.249 · 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