Entomological Opportunities and Challenges for Sustainable Viticulture in a Global Market
Bibliographic record
Abstract
Viticulture has experienced dramatic global growth in acreage and value. As the international exchange of goods has increased, so too has the market demand for sustainably produced products. Both elements redefine the entomological challenges posed to viticulture and have stimulated significant advances in arthropod pest control programs. Vineyard managers on all continents are increasingly combating invasive species, resulting in the adoption of novel insecticides, semiochemicals, and molecular tools to support sustainable viticulture. At the local level, vineyard management practices consider factors such as the surrounding natural ecosystem, risk to fish populations, and air quality. Coordinated multinational responses to pest invasion have been highly effective and have, for example, resulted in eradication of the moth Lobesia botrana from California vineyards, a pest found in 2009 and eradicated by 2016. At the global level, the shared pests and solutions for their suppression will play an increasing role in delivering internationally sensitive pest management programs that respond to invasive pests, climate change, novel vector and pathogen relationships, and pesticide restrictions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".