Effect of seed damage and metalaxyl seed treatment on pythium seedling blight and seed yield of field pea
Bibliographic record
Abstract
When cool, wet conditions persist after planting, Pythium spp. can be an important constraint to stand establishment in field pea. Laboratory studies and field trials were conducted over 3 yr to assess the impact and evaluate the interactions of Pythium spp., metalaxyl seed treatment and damage to seed on seedling establishment, root rot severity and seed yield of field pea. Seedling emergence, seedling size, and seed yield were reduced by inoculation with Pythium spp. and by mechanical damage to the seed. Fungicide seed treatment reduced the impact of seed damage, but did not always restore seedling emergence and seed yield to the same level as from undamaged seed. Undamaged seed treated with metalaxyl was not affected by inoculation with Pythium spp. Differences among cultivars, although often significant, were small relative to the effect of seed injury. Laboratory studies showed a negative linear relationship between inoculum concentration and emergence from untreated seed. They also showed that Pythium spp. had a similar impact on seedling emergence in cool (20/10°C day/night) and cold (12/6°C) soils. This study showed that planting fungicide-treated, high-quality field seed was an effective means of maximizing emergence and stand establishment for commercial field pea production. Key words: Pisum sativum, seed vigour, metalaxyl, Pythium, seed damage
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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) | 0.002 | 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".