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Record W175052407 · doi:10.1111/ajgw.12195

Trends in the composition of Australian wine 1984-2014

2015· article· en· W175052407 on OpenAlexfundno aff
Peter Godden

Bibliographic record

VenueAustralian Journal of Grape and Wine Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsnot available
FundersAlberta Water Research Institute
KeywordsWineComposition (language)AdvertisingBusinessFood scienceChemistryArtLiterature

Abstract

fetched live from OpenAlex

Data for wines from the 2004–2014 vintages were collated from the database of analytical results of The Australian Wine Research Institute's Commercial Services Group and are reviewed in the context of historical trends in wine composition. Data for those 11 vintages were generated from 24 066 commercially bottled Australian table wines (8384 white and rosé wines, and 15 682 red wines), which were submitted to The Australian Wine Research Institute for analysis required to comply with the export/import requirements of destination countries. The wines include multiple vintages of a broad cross-section of Australian wines from commodity to icon status, and producers of all sizes. The wines represent a broad geographical and cultivar spread, with the proportion of wines of each cultivar strongly correlating with the planted vineyard area. The data relate to the compositional variables: alcohol, glucose plus fructose, total dry extract excluding alcohol, sugar and volatile acidity (TDE), titratable acidity at pH 8.2, pH, free sulfur dioxide (SO2), total SO2, bound SO2, and the ratio of free to total SO2. Certain previously identified year-on-year trends have continued, and in some cases appear to have accelerated in the most recent vintages, particularly with increasing glucose plus fructose concentration and pH in red wines. In other cases, previously identified trends appear to have gone into reverse, notably the rise in alcohol concentration in red wines, and for a period, rising TDE in red wines, and one new trend in red wines is apparent, namely decreasing titrable acidity. There is some indication that new trends are also apparent with white and rosé wines, namely decreasing alcohol, increasing glucose plus fructose, increasing pH and an apparent downward shift in TDE, although ANOVA indicates little statistical significance in those trends. Data related to the concentration of SO2 demonstrate upward trends in free SO2 for white and rosé, and red wines. This has occurred with a concurrent decrease in the concentration of total SO2 in red wines for the most recent vintages, leading to a consequent rise in the ratio of free to total SO2. A rise in the ratio of free to total SO2 is also seen in white and rosé wines due to increasing free SO2 concentration. Overall white and rosé wines display fewer upward or downward trends compared with that of red wines, but greater year-on-year variability in the data.

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.157
GPT teacher head0.373
Teacher spread0.216 · 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 designNot applicable
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

Citations22
Published2015
Admission routes1
Has abstractyes

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