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Record W2513289027

Le Polyéthylène Téréphtalate, un emballage pour le vin de qualité ? Partie 2/2 : Évolution au cours du stockage d’un vin rosé conditionné en bouteilles PET avec absorbeur d’oxygène

2016· preprint· en· W2513289027 on OpenAlexaff
Clara Dombre, Jérémie Wirth, Marie Toussaint, Camille Lixon, Arnaud Verbaere, Nicolas Sommerer, Jean‐Claude Boulet, Soline Caillé, Véronique Cheynier, Peggy Rigou, Alain Samson, Jean‐Michel Salmon, Jean-Claude Vidal, Stéphane Marais, Yves Gerand, Philippe Roux, Marie Helene Lemaistre, Alain Bobé, Perrine Languet, Pascale Chalier

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2016
Typepreprint
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsGDG Environnement
Fundersnot available
KeywordsPolitical scienceArtHumanitiesMaterials science
DOInot available

Abstract

fetched live from OpenAlex

A Cinsault rosé wine was packaged in glass bottles and PET with 1 and 3 % oxygen scavenger and stored 12 months. Using PET with 3 % scavenger did not allow the maintenance of CO2 but limited oxygen entry protecting SO2 as for wine in glass bottle. The loss of tannins and anthocyanins was more pronounced for wines packaged in PET 1 %, whereas it was the same for wine in glass or PET 3 %. Likewise, aroma compounds aging markers as dioxanes were found in similar manner in wine stored in PET with 3 % and in the glass while the oxidation of methionol is clearly higher in the PET 1 %. Sensory analysis has confirmed these results with a wine with a lighter color and honey and cooked wine smells more pronounced when packaged in PET with 1 % absorber compared to glass packaging and PET with 3 % oxygen scavenger.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueHAL (Le Centre pour la Communication Scientifique Directe)Same topicNeurological Disease Mechanisms and TreatmentsFrench-language works237,207