Occurrence and Distribution of Common Diseases and Pests of U.S. Cannabis: A Survey
Bibliographic record
Abstract
Hemp and marijuana, both Cannabis sativa L., are revitalized crops to U.S. agricultural and horticultural industries. Hemp (Δ⁹-Tetrahydrocannabinol content < 0.3%) was reintroduced in 2014 under a pilot research program and legalized in 2018. Hemp can now be grown in all 50 states. Marijuana (Δ⁹-THC content > 0.3%), although classified as a Schedule I narcotic by the U.S. Drug Enforcement Administration, is legal in 37 states for medical and/or recreational use. Although C. sativa is often promoted as a pest-free crop, multiple diseases and arthropod pests have been identified and confirmed in recent years. There are limited options for control of diseases and pests affecting hemp. A survey of diagnosticians, researchers, and industry leaders conducted from 2021 to 2022 sought to determine the distribution and occurrence of 76 common diseases and pests on C. sativa across the United States. A total of 148 responses were collected and grouped by U.S. region: Western, Great Plains, North Central, Northeastern, and Southern. Survey results suggest that whereas some pathogens and pests are widely distributed across the United States, others occur more frequently in specific regions. This finding may indicate variations in economic importance by region. Results from this survey provide a foundation for regional and national prioritization of research and regulatory activities.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".