Control, by microbial seed treatment, of dampingoff caused by<i>Pythium</i>sp. on canola, safflower, dry pea, and sugar beet
Bibliographic record
Abstract
Indoor and field experiments were conducted to determine the effects of seed treatment with strains of rhizosphere bacteria collected from southern and central Alberta for control of damping-off of sugar beet, canola, safflower, and dry pea caused by Pythium sp. Indoor experiments showed that 12 strains of rhizobacteria, including Pseudomonas fluorescens (708, 1-2, 1105, 1809, 2106, and 2202), Bacillus cereus PS1, Bacillus megaterium SB6, Arthrobacter sp. 2101, Pantoea agglomerans (909, 2-2), and Erwinia rhapontici A123, were effective in controlling damping-off of sugar beet. These strains varied in their ability to suppress mycelial growth of Pythium sp. group G, in vitro, and in their ability to secrete extracellular protease. Strains of Pseudomonas fluorescens (708, 1-2, 2202), B. cereus PS1, E. rhapontici A123, and Pantoea agglomerans 2-2 were effective seed-treatment agents for control of damping-off of canola, safflower, dry pea, and sugar beet in fields naturally infested with Pythium spp., although there were some differences in efficacy among the strains for each of the crops. Seed treatment with combination of Pseudomonas fluorescens 708, B. cereus PS1, or E. rhapontici A123 and the fungicide Thiram™ did not improve dry pea emergence compared with the fungicide control under field conditions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".