Evaluating return-on-investment from vine to wine: sensory evaluation and consumer willingness-to-pay of vineyard management strategies for Vancouver Island Pinot gris
Bibliographic record
Abstract
Cluster thinning, leaf removal and kaolin application are three commonly employed means of modifying fruit zone microclimates assumed to improve grape and wine quality. High labour costs in addition to the potential for location and varietal-specific responses make the benefits of these practices equivocal. To explore this question, I employed a stratified random block design in two Vancouver Island commercial vineyards where Pinot gris vines were manipulated with one of four experimental treatments: control, cluster thinning to one cluster per shoot, heavy leaf removal with a Kaolin clay application, or a combined heavy leaf removal – cluster thinning - Kaolin treatment. The resulting wines were subjected to detailed sensory evaluations, consumer valuation, and a cost-benefit analysis to determine their respective return-on-investment. Results indicate that despite apparent sensory changes driven by vineyard treatments, the lack of any perceived added value suggests that regional producers of Pinot gris should avoid using the assessed treatments as strategies to increase wine quality. The cost-benefit analysis revealed that heavy leaf removal combined with Kaolin clay application may provide a benefit outside of changes to wine quality. The demonstrable improvement in growing conditions under this treatment resulted in a significant decrease in rot pressure. This suggests that the treatment may be a viable option for increasing usable yields of Pinot gris without placing an insurmountable financial cost on the producer.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".