Effects of a foliar fertilizer containing boron on the development of Sclerotinia stem rot (<i>Sclerotinia sclerotiorum</i>) on canola (<i>Brassica napus</i> L.) leaves
Bibliographic record
Abstract
Abstract Sclerotinia stem rot (Sclerotinia sclerotiorum Lib. De Bary) is one of the most destructive fungal diseases on canola (Brassica napus L.). The effect of a foliar fertilizer containing 3% boron (Active Flower™ [AF]) in reducing disease severity was evaluated. AF at 0.1, 0.3 and 0.5 ml/100 ml was first tested for growth inhibition of S. sclerotiorum in potato dextrose broth. Growth was reduced at 0.5 ml/100 ml by around 90%. Boric acid (BA), an important component of AF, was tested against fungal growth at 10 ml/L, and no significant effect (p = .05) was found. Foliar applications of AF and AF formulation that did not contain boron at 0.1, 0.3 and 0.5 ml/100 ml were made weekly to canola ‘Westar’ grown under greenhouse conditions. Treatments were also made with BA at 10 ml/L to canola plants. After four applications, AF at 0.5 ml/100 ml and BA at 10 ml/L enhanced boron levels in leaves by fivefold and threefold, respectively, compared with the control. Lesion size of S. sclerotiorum on detached leaves was significantly (p < .05) reduced by AF at 0.5 ml/100 ml, but lesion size was not reduced on AFWB‐treated leaves. Experiments were repeated twice with the same results. Levels of phenolic compounds in leaves treated with 0.5 ml/100 ml AF were enhanced by twofold compared with the control. There were no significant differences in lignin, peroxidase (POD) or polyphenoloxidase (PPO) between the control and AF treatments. These results suggest that enhanced boron levels in canola leaves were associated with a suppressive effect on disease due to S. sclerotiorum.
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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 | 0.001 |
| 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".