Using GPS, GIS & remote sensing to understand Niagara Terroir : Pinot noir in the Four Mile Creek & St. David's Bench sub-appellations
Bibliographic record
Abstract
The relationships between vine water status, soil texture, and vine size were observed in four Niagara, Ontario Pinot noir vineyards in 2008 and 2009. The vineyards were divided into water status zones using geographic information systems (GIS) software to map the seasonal mean midday leaf water potential (,P), and dormant pruning shoot weights following the 2008 season. Fruit was harvested from all sentinel vines, bulked by water status zones and made into wine. Sensory analysis included a multidimensional sorting (MDS) task and descriptive analysis (DA) of the 2008 wines. Airborne multispectral images, with a spatial resolution of 38 cm, were captured four times in 2008 and three times in 2009, with the final flights around veraison. A semi-automatic process was developed to extract NDVI from the images, and a masking procedure was identified to create a vine-only NDVI image. 2008 and 2009 were cooler and wetter than mean years, and the range of water status zones was narrow. Yield per vine, vine size, anthocyanins and phenols were the least consistent variables. Divided by water status or vine size, there were no variables with differences between zones in all four vineyards in either year. Wines were not different between water status zones in any chemical analysis, and HPLC revealed that there were no differences in individual anthocyanins or phenolic compounds between water status zones within the vineyard sites. There were some notable correlations between vineyard and grape composition variables, and spatial trends were observed to be qualitatively related for many of the variables. The MDS task revealed that wines from each vineyard were more affected by random fermentation effects than water status effects. This was confirmed by the DA; there were no differences between wines from the water status zones within vineyard sites for any attribute. Remotely sensed NDVI (normalized difference vegetation index) correlated reasonably well with a number of grape composition variables, as well as soil type. Resampling to a lower spatial resolution did not appreciably affect the strength of correlations, and corresponded to the information contained in the masked images, while maintaining the range of values of NDVI. This study showed that in cool climates, there is the potential for using precision viticulture techniques to understand the variability in vineyards, but the variable weather presents a challenge for understanding the driving forces of that variability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".