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Record W2036095546 · doi:10.5539/jfr.v2n1p140

Enumeration and Identification of 4-Ethylphenol Producing Yeasts Recovered from the Wood of Wine Ageing Barriques after Different Sanitation Treatments

2013· article· en· W2036095546 on OpenAlexvenueno aff
André Barata, Paulo Laureano, Isabella D’Antuono, Patricia Martorell, Henrik Stender, Manuel Malfeito‐Ferreira, Amparo Querol, V. Loureiro

Bibliographic record

VenueJournal of Food Research · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsWineSteamingFood scienceWineryEnumerationMost probable numberBiologyAnimal scienceChemistryMathematicsBacteria

Abstract

fetched live from OpenAlex

<p>Aims: This work was aimed at the evaluation of several sanitation procedures on the reduction of total microbial flora and of <em>D. bruxellensis</em> recovered from the inner layers of the barrique’s wood.</p> <p>Methods: A group of used oak barrels tainted by 4-ethylphenol and contaminated with <em>D. bruxellensis</em> were differently sanitized and, afterwards, were dismantled to analyse samples of shaves taken from wood surfaces at different depths. Microbial counts were obtained by the Most Probable Number Technique using broths of general purpose medium and of <em>Dekkera</em>/<em>Brettanomyces </em>differential medium (DBDM).</p> <p>Results: The least inefficient treatment included barrique steaming at low pressure. Uncontaminated samples were only detected under this treatment and in the upper level (0-2 mm) of the staves. With this treatment complete destruction of the contaminating flora was not achieved in any level of stave side surfaces and in grooves. The presence of <em>D. bruxellensis </em>was detected in depths up to 6-8 mm in the wood corresponding to the maximum level of wine penetration.</p> <p>Significance: this work demonstrated that even after current sanitation procedures barriques used in wine maturation pose a severe risk to wine stability due to the presence of <em>D. bruxellensis</em>.</p>

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.

How this classification was reachedexpand

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 score0.773
Threshold uncertainty score0.184

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)0.0000.000

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 head0.055
GPT teacher head0.300
Teacher spread0.245 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2013
Admission routes1
Has abstractyes

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