ITK Vigne, a decision-support tool to adapt wine production to climate change, with or without irrigation
Bibliographic record
Abstract
Climate change is an important challenge for most wine producing countries, since wine quality is closely linked to terroir, an interaction between soil, climate and training method. Hence a change in climate should induce a change in viticultural techniques, if one wants to keep the typicity of his appellation wines. In the Mediterranean region, the evolution towards warmer and drier summers has a strong and generally unfavourable influence on vineyard productivity (i.e.: yield and quality). Optimal vine water status dynamics have been therefore defined to produce distinctive wine profiles. However, the latter approach is rarely implemented in practice, since classical methods to measure grapevine water status are either too tedious or too expensive to be implemented on a large scale.To overcome those limits, a model-based decision support system (DSS), named iTKVigne, has been developed by a consortium led by ITK Company. The models included in this DSS were specifically adapted from former work done by INRA and CIRAD, and have been tested in the field from 2009 to 2013. After an initial calibration, this DSS has proven to be able to provide a very satisfactory estimation of pre-dawn water potential according to the soil, climate and vineyard training method at significantly lower cost than classical techniques. Unlike physical water status measurements, iTKVigne does not only allow a more accurate and water saving irrigation (irrigation quantities are generally reduced by about 15% when compared to current irrigation practices), it also allows adapting the training method according to the desired production objective, for winegrowers who cannot or do not want to irrigate. It may also be used as a tool to forecast on a production objective basis the future water requirements of new irrigation perimeters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".