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Record W3095340190 · doi:10.1002/cyto.a.24258

Evaluating the Cytometric Detection and Enumeration of the Wine Bacterium, <i>Oenococcus oeni</i>

2020· article· en· W3095340190 on OpenAlex
Louise Bartle, James G. Mitchell, James Paterson

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

VenueCytometry Part A · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsUniversité de Sherbrooke
FundersWine AustraliaUniversity of AdelaideAustralian Government
KeywordsOenococcus oeniWineEnumerationFlow cytometryCytometryBacteriaYeastBiologyMalolactic fermentationFood scienceChemistryBiochemistryMolecular biologyMathematicsGenetics

Abstract

fetched live from OpenAlex

Flow cytometry is a high-throughput tool for determining microbial abundance in a range of medical, environmental, and food-related samples. For wine, determining the abundance of Saccharomyces cerevisiae is well-defined and reliable. However, for the most common wine bacterium, Oenococcus oeni, using flow cytometry to determine cell concentration poses some challenges. O. oeni most often occurs in doublets or chains of varying lengths that can be greater than seven cells. This wine bacterium is also small, at 0.2-0.6 μm and may exhibit a range of morphologies including binary fission and aggregated complexes. This work demonstrates a straightforward approach to determining the suitability of flow cytometry for the chain-forming bacteria, O. oeni, and considerations when using flow cytometry for the enumeration of small microorganisms (<0.5 μm). © 2020 International Society for Advancement of Cytometry.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.082
GPT teacher head0.288
Teacher spread0.206 · 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