Effects of inoculum density, temperature, seeding depth, seeding date and fungicidal seed treatment on the impact of <i>Rhizoctonia solani</i> on lentil
Bibliographic record
Abstract
Rhizoctonia solani causes seedling blight and root rot in lentil, which reduces plant populations and the vigour and yield of surviving plants. Factors in the seedling environment, such as inoculum density, temperature, seeding depth, seeding date, and fungicidal seed treatment were studied to determine the degree to which they affect the impact of R. solani on lentil seedlings. Survival of lentil plants was evaluated after planting into soil artificially inoculated with various concentrations of a highly aggressive isolate of R. solani (AG-4). Emergence, seedling survival and shoot dry matter production decreased with increasing inoculum density, but these declines varied with temperature. Low soil temperatures delayed the emergence of lentil seedlings in non-inoculated soil, but in inoculated soils, emergence was inhibited with increasing temperatures. Depth of seeding did not affect seedling establishment, but root rot severity increased with depth of seeding in a growth cabinet trial. Root nodulation was reduced as root rot severity increased. In field experiments carried out over 3 station years, seeding date had a substantial effect on seedling emergence and yield of inoculated treatments, but the trends were not consistent between sites. In field assessments of fungicide efficacy, treatment of seed with thiabendazole plus carbathiin (Crown) and carbathiin plus thiram (Vitaflo 280) improved seedling establishment relative to the inoculated control. Key words: Lens culinaris, damping-off, root rot, seeding date, fungicide seed treatment depth of seeding, thiabendazole, carbathiin, thiram
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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".