Geology and Wine 8. Modeling Viticultural Landscapes: A GIS Analysis of the Terroir Potential in the Umpqua Valley of Oregon
Bibliographic record
Abstract
Terroir is a holistic concept that relates to both environmental and cultural factors that together influence the grape growing to wine production continuum. The physical factors that influence the process include matching a given grape variety to its ideal climate along with optimum site characteristics of elevation, slope, aspect, and soil. While some regions have had hundreds and even thousands of years to define, develop, and understand their best terroir, newer regions typically face a trial and error stage of finding the best variety and terroir match. This research facilitates the process by modeling the climate and landscape in a relatively young grape growing region in Oregon, the Umpqua Valley appellation. The result is an inventory of land suitability that provides both existing and new growers greater insight into the best terroirs of the region. SOMMAIRE Le terroir est un concept holiste de facteurs environnementaux et culturels agissant sur un continuum s'etendant de la croissance de la vigne a la vinification. Dans le domaine des facteurs physiques, il faut trouver la combinaison ideale entre la variete du raisin d'une part, et le climat et les caracteristiques du site de culture comme l'elevation, la pente, l'aspect et le type de sol, d'autre part. Alors qu'en certaines regions, on a eu des centaines, voire des milliers d'annee pour definir, developper et definir le terroir ideal, dans les regions nouvelles, on doit proceder par essais et erreur pour trouver le meilleur appariement raisin et terroir. La recherche decrite ci-contre entend faciliter ce processus de mariage ideal en modelisant les facteurs du climat et du paysage dans une region viticole relativement jeune de l'Oregon, celle de la vallee de Umpqua. Le resultat obtenu est un inventaire des terrains propices, ce qui fait aussi bien l'affaire des vignerons etablis que des nouveaux vignerons dans leur quete des meilleurs terroirs de la region.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".