Assessment of <i>Fusarium avenaceum</i> inoculation methods for consistent development of pea root rot under greenhouse conditions
Bibliographic record
Abstract
Fusarium avenaceum (Fave) is the most dominant pathogen of the pea root rot complex in the Canadian prairies. Its increasing prevalence and broad host range present significant challenges, underscoring the urgent need for research to develop effective management strategies including breeding for resistance. A critical aspect of efficient pathogen studies and screening pea lines for resistance under controlled conditions is selecting an appropriate inoculation method. In previous experiments, we observed that classical Fave inoculation methods such as seed inoculation either lacked uniform infection or killed plants before emergence. Therefore, we tested different Fave conidia inoculation methods to assess disease development in various pea lines after Fave exposure and refined these methods to develop an optimized indoor screening protocol for Fave. Among the tested methods, soil inoculation with Fave conidia resulted in the most consistent and uniform disease symptoms across repeated experiments. This method could be most effective for germplasm screening for partial resistance. A modified seed inoculation method also resulted in moderate disease levels, making it the most effective for testing the virulence of different isolates of Fave on the same genotype. Root soaking resulted in uniform but low disease severity and can be used for studying host–pathogen interactions where observation during the infection process is important.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".