Root and crown rot pathogens causing wilt symptoms on field-grown marijuana (<i>Cannabis sativa</i> L.) plants
Bibliographic record
Abstract
Yellowing and wilting symptoms on field-grown Cannabis sativa (cannabis) plants followed by total plant collapse under conditions of extreme hot weather were observed in northern California in 2017. The crown regions of affected plants were dark and sunken and internal tissue discolouration extended 10–15 cm above the soil surface. Isolations made from the pith, vascular and cortical tissues in the crown region yielded Fusarium oxysporum (40% frequency), F. brachygibbosum (28% frequency), Pythium aphanidermatum (22% frequency), Fusarium solani and F. equiseti (5% frequency each). Pathogenicity tests were conducted on rooted plantlets to establish the extent of root and crown decay, as well as on mature stems to determine the extent of stem tissue colonization caused by these species. Extensive reduction in root length was caused by F. solani, F. oxysporum, F. brachygibbosum and P. aphanidermatum and wounding significantly enhanced disease development. Stem tissue colonization by these pathogens at wound sites was similarly extensive. Isolates of F. equiseti were non-pathogenic. Both F. solani and P. aphanidermatum caused plant mortality within 6–10 weeks following inoculation. In phylogenetic analyses using the internal transcribed spacer (ITS) rDNA region and the elongation factor 1 (EF-1α) region, F. oxysporum isolates from cannabis plants in northern California were grouped separately from all other formae speciales and from isolates previously recovered from British Columbia. Two isolates of F. brachygibbosum were identical to an isolate previously reported to infect almond stems in cold storage and field-grown seedlings in northern California. These findings indicate that a complex of pathogens potentially can cause root and crown rot under field conditions, resulting in wilt symptoms and collapse of cannabis plants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".