To the Root of the Problem: Preventing Excess Copper Waste and Remediation, by Targeted Treatment of Downy Mildew in Grape Crops (End of 2020 report)
Bibliographic record
Abstract
Grapes are an important Canadian crop. Canadian winemaking industries rely on grape growers. However, grape crops are threatened by a mould that causes downy mildew. This disease spreads onto grape leaves and if left untreated can kill much of the plant. The grape fruit becomes covered in a dense carpet of mould tissue, and the crop is spoiled and lost. Not just a Canadian problem, downy mildew has become an agricultural concern for vineyards world-wide. Global warming is expected to make the incidence and impact of downy mildew worse. A good treatment is copper sulfate, which is sprayed onto grape plants early in the season to suppress downy mildew growth. However, copper is a toxic element that accumulates in the environment and may impact both the vineyard ecosystem and connected environments. CleanForm Science CA is a Canadian company that wants to reduce the impact of downy mildew, preventing loss of crops, and protecting economic investments. The Sabatinos lab at Ryerson University partners with CleanForm Science CA to develop a new testing kit for downy mildew that will help target new treatments. Confidential report prepared for CleanForm Science CA
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".