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Record W2735965484 · doi:10.7150/ntno.20301

Recent Advances in Biosensor Development for Foodborne Virus Detection

2017· review· en· W2735965484 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNanotheranostics · 2017
Typereview
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsRisk analysis (engineering)Emerging technologiesBiotechnologyComputer scienceBiochemical engineeringBusinessBiologyEngineering

Abstract

fetched live from OpenAlex

Outbreaks of foodborne diseases related to fresh produce have been increasing in North America and Europe. Viral foodborne pathogens are poorly understood, suffering from insufficient awareness and surveillance due to the limits on knowledge, availability, and costs of related technologies and devices. Current foodborne viruses are emphasized and newly emerging foodborne viruses are beginning to attract interest. To face current challenges regarding foodborne pathogens, a point-of-care (POC) concept has been introduced to food testing technology and device. POC device development involves technologies such as microfluidics, nanomaterials, biosensors and other advanced techniques. These advanced technologies, together with the challenges in developing foodborne virus detection assays and devices, are described and analysed in this critical review. Advanced technologies provide a path forward for foodborne virus detection, but more research and development will be needed to provide the level of manufacturing capacity required.

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 score0.995
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 integrity0.0000.000
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 head0.066
GPT teacher head0.325
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