Building a more predictive model of terroir for the Anthropocene
Bibliographic record
Abstract
Societal Impact Statement Climate change and shifts in management have produced a new world of challenges for most winegrape growers, including increasing sugar levels and altered pest and weather regimes. This new world has elevated the importance of exploiting the genetic diversity of winegrapes and wild Vitis relatives. Here, we argue that how well we exploit genetic diversity will depend on our understanding of “terroir,” especially how the environment interacts with different cultivars (varieties). Addressing this challenge could revolutionize grape growing but will require researchers and growers to work together on new approaches to collect and synthesize data. Summary Anthropogenic climate change has produced a new world of challenges for many wild species and crops, including shifted ranges and phenology, alongside altered pest and weather regimes. These impacts are especially apparent in winegrapes, which has elevated the importance of exploiting the genetic diversity of the crop and its wild relatives. Here, we argue that how well growers can exploit this diversity will depend on our understanding of “terroir,” especially how the environment interacts with different genotypes (varieties). We argue that a globally predictive framework for winegrowing is possible but requires greater efforts to integrate the genetic diversity of vinifera and other Vitis species. This includes distributed approaches and efforts to synthesize data, especially across rootstock scion experiments, which currently focus on one site alone. The opportunity and need for this transition, however, has never been greater, as climate change means the climate of each region is shifting over time—often towards the climate of another region, making the need to build a predictive framework that works across regions increasingly urgent.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".