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Explore the impact of free sulfur dioxide on red and white wine

2024· article· en· W4401035603 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.

Bibliographic record

VenueTheoretical and Natural Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWhite WineSulfur dioxideWineWhite (mutation)SulfurEnvironmental scienceChemistryFood scienceInorganic chemistryOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Today, red and white wine are essential symbols that motivate the world’s economy and are cultural symbols. Thus, the taste and process of alcohol during fermentation have become highly evocative to producers today. In this way, this article focuses on the effect of free sulfur dioxide on the concentration of fixed and volatile acids since fixed and volatile acids have a high impact on the taste of red and white wine, according to scientific research. This article also focuses on the PH value of both wines to investigate whether the free sulphate dioxide has a positive effect on the PH acidity of red and white wine. By paying attention to the acidity, the producers can further investigate the health impact of both wines and provide better choices and plans for the consumers. Using Cortez’s data, this article matches the linear regression model to compare the fixed and volatile groups between red and white wine. PH values of both wines are also laid out in the final linear regression model group. The linear regression model tests the difference between free sulfur dioxide’s effects on the two wine categories. Lastly, the RMSE value has been used to test whether the result is reliable.

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)0.001
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 score0.850
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
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 head0.019
GPT teacher head0.296
Teacher spread0.276 · 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