Response of Barley Genotypes to Fusarium Head Blight under Natural Infection and Artificial Inoculation Conditions
Bibliographic record
Abstract
Forty-eight spring barley genotypes were evaluated for deoxynivalenol (DON) concentration under natural infection across 5 years at Harrington, Prince Edward Island. These genotypes were also evaluated for Fusarium head blight (FHB) severity and DON concentration under field nurseries with artificial inoculation of Fusarium graminearum by the grain spawn method across 2 years at Ottawa, Ontario, and one year at Hangzhou, China. Additionally, these genotypes were also evaluated for FHB severity under greenhouse conditions with artificial inoculation of F. graminearum by conidial suspension spray method across 3 years at Ottawa, Ontario. The objective of the study was to investigate if reactions of barley genotypes to artificial FHB inoculation correlate with reactions to natural FHB infection. DON concentration under natural infection was positively correlated with DON concentration (r = 0.47, P < 0.01) and FHB incidence (r = 0.56, P < 0.01) in the artificially inoculated nursery with grain spawn method. Therefore, the grain spawn method can be used to effectively screen for low DON. FHB severity, generated from greenhouse spray, however, was not correlated with DON concentration (r = 0.12, P > 0.05) under natural infection and it was not correlated with DON concentration (r = -0.23, P > 0.05) and FHB incidence (r = 0.19, P > 0.05) in the artificially inoculated nursery with grain spawn method. FHB severity, DON concentration, and yield were affected by year, genotype, and the genotype × year interaction. The effectiveness of greenhouse spray inoculation for indirect selection for low DON concentration requires further studies. Nine of the 48 genotypes were found to contain low DON under natural infection. Island barley had low DON and also had high yield.
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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) | 0.000 | 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".