Phytotoxicity of Herbicides in Seedlings of Sweet Passion Fruit
Bibliographic record
Abstract
The culture of sweet passion fruit has been increasingly studied and exploited commercially in Brazil, with emphasis on the State of São Paulo. This is due to the high value achieved in the market of fresh fruit and be considered a food with functional properties beneficial to human health. However, the cultivation on a large scale finds some obstacles, as the control of weeds with the use of herbicides. In this sense, the objective of this work was to evaluate the phytotoxicity of seven herbicides with different mechanisms of action in seedlings of sweet passion fruit. Different variables were assessed: initial plant height (IPH) and final plant height (FPH); initial number of sheets (INS) and end number of sheets (ENS); intoxication of plants (INTO); fresh leaf mass (FLM) and dry leaf mass (DLM); fresh stem mass (FSM); dry steam mass (DSM); fresh root mass (FRM) and dry root mass (DRM) and total dry mass (TDM). Chlorimuron-ethyl (ALS inhibitor), glyphosate (EPSP synthase inhibitor), paraquat (photosystem I inhibitor) and atrazine (photosystem II inhibitor) have the higher effect on the variables analyzed, and they caused some kind of intoxication in the plants of sweet passion fruit. Conversely, haloxyfop-p-methyl (ACCase inhibitor) and fomesafen (protox inhibitor) showed a lesser effect on the sweet passion fruit plants.
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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".