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Sensory analysis of Ontario Riesling wines from various water status zones

2018· article· en· W2820264551 on OpenAlex
James Willwerth, Andrew G. Reynolds, Isabelle Lesschaeve

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOENO One · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVineyardTerroirVineHorticultureWineSensory analysisLianaGeographyEnvironmental scienceBiologyBotanyFood science

Abstract

fetched live from OpenAlex

Aims: Determinants of the terroir effect in Riesling were sought by choosing vine water status as a major factor. It was hypothesized that consistent water status zones could be identified within vineyards, and, differences in wine sensory attributes could be related to vine water status.Methods and results: To test our hypothesis, 10 Riesling vineyards representative of each Ontario Vintners Quality Alliance sub-appellation were selected. Vineyards were delineated using global positioning systems and 75 to 80 sentinel vines were geo-referenced within a sampling grid for data collection. During 2005 to 2007, vine water status measurements [leaf water potential (ψ)] were collected bi-weekly from a subset of these sentinel vines. Vines were categorized into “low” and “high” leaf ψ zones within each vineyard through use of geospatial maps and replicate wines were made from each zone. Wines from similar leaf ψ zones had comparable sensory properties ascertained through sorting tasks and multidimensional scaling (2005, 2006). Descriptive analysis further indicated that water status affected wine sensory profiles, and attributes differed for wines from discrete leaf ψ zones. Multivariate analyses associated specific sensory attributes with wines of different leaf ψ zones. Several attributes differed between leaf ψ zones within multiple vineyard sites despite different growing seasons. Wines produced from vines with leaf ψ >-1.0 MPa had highest vegetal aromas whereas those with leaf ψ <-1.3 MPa were highest in honey, petrol and tropical fruit flavors. Vines under mild water deficit had highest honey, mineral, and petrol and lowest vegetal aromas.Conclusion: Results indicate that water status has a profound impact on sensory characteristics of Riesling wines and that there may be a quality threshold for optimum water status.Significance and impact of the study: These data suggest that vine water status has a substantial impact on the sensory properties of Riesling wines. Variability of leaf ψ within vineyards can lead to wines that differ in their sensory profiles. These findings were consistent among vineyards across the Niagara Peninsula. These strong relationships between leaf ψ and sensory attributes of Riesling suggest that vine water status is a major basis for the terroir effect.

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 categoriesInsufficient payload (model declined to judge)
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 score0.995

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)0.0060.000

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 head0.048
GPT teacher head0.260
Teacher spread0.212 · 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