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Record W3135143584 · doi:10.1007/s12560-021-09466-0

Norovirus Extraction from Frozen Raspberries Using Magnetic Silica Beads

2021· article· en· W3135143584 on OpenAlex
Philippe Raymond, Sylvianne Paul, André Perron, Louise Deschênes

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

VenueFood and Environmental Virology · 2021
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsCegep de Saint HyacintheAgriculture and Agri-Food CanadaCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsNorovirusExtraction (chemistry)BiologyChromatographyVirologyChemistryVirus

Abstract

fetched live from OpenAlex

Human noroviruses (HuNoV) are among the main causes of acute gastroenteritis worldwide. Frozen raspberries have been linked to several HuNoV food-related outbreaks. However, the extraction of HuNoV RNA from frozen raspberries remains challenging. Recovery yields are low, and real-time quantitative reverse transcriptase PCR (RT-qPCR) inhibitors limit the sensitivity of the detection methodologies. A new approach using fine magnetic silica beads was developed for the extraction of HuNoV spiked on frozen raspberries. Relatively low recovery yields were observed with both the magnetic silica bead and the reference ISO 15216-1:2017 methods. High RT-qPCR inhibition was observed with the ISO 15216-1:2017 recommended amplification kit but could be reduced by using an alternative kit. Reducing RT-qPCR inhibition is important to limit the number of inconclusive HuNoV assays thus increasing the capacity to assess the HuNoV prevalence in frozen raspberries.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.023
GPT teacher head0.275
Teacher spread0.252 · 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