Inducing Resistance in Heliconia psittacorum cv. Golden Torch to Naturally Occurring Leaf Diseases
Bibliographic record
Abstract
One of the bottlenecks to heliconia production is leaf diseases, which have the main characteristic of forming necrotic spots, that can reduce photosynthesis, damage the heliconia bracts and make the flowers unsuitable for commercialisation. The objective of the present study was to identify inducers capable of inducing resistance in H. psittacorum cv. Golden Torch, assessing by enzymatic action the reduction in the severity of the fungus complex associated to the cultivation of this species and verifying the action of the severity in relation to the net photosynthesis rate of the plants. The inducers were applied to the plants using a back spray and during application the plots were protected to prevent the products from drifting. The concentrations applied were Agro-Mos® (10 ml L-1), Bion® (0.5 g L-1), Quartz® (40 ml L-1), potassium phosphite (3 g L-1) and calcium phosphite (3 g L-1). During the experimental cultivation, the severity caused by the fungus complex and the photosynthetic rate of the plants were assessed and plant material was collected to analyse the enzymatic activity. The results indicated that the inducers Bion® and the phosphites applied reduced the fungus complex severity, increased the activity of peroxidase, polifenoloxidase and β-1.3 gluconase but did not cause alterations in the net photosynthesis rate of the plants. The value found for β > 1 suggested that the visual estimation of the fungus complex severity is a good visual indicator of the effect of the plant pathogens on the host photosynthetic rate. The results are concrete responses to producers in the sector on management alternatives for diseases associated to heliconia cultivation.
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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".