Seasonality affects phytotoxic potential of five native species of Neotropical savanna
Bibliographic record
Abstract
Phytotoxic secondary metabolites produced by plants have been studied as possible sources of bioherbicides. However, several environmental factors can change the secondary metabolism of plants, and thus, the production of these compounds. Among these factors, seasonality can be a source of variation in the content of all classes of secondary metabolites. In this study, we evaluated the phytotoxic effect of aqueous extracts of leaves from Byrsonima intermedia A. Juss. (Malpighiaceae), Gochnatia polymorpha (Less.) Cabrera (Asteraceae), Luehea candicans Mart. (Tiliaceae), Miconia chamissois Naudin (Melastomataceae), and Qualea cordata Spreng (Vochysiaceae) (species of the Brazilian savanna), collected both during the dry and the rainy season, on germination and seedling growth of maize and cucumber. The analysed parameters were affected by all leaf extracts collected during both seasons. However, a greater phytotoxic effect was observed when leaves were collected during the dry or during the rainy season, depending on the savanna species studied, on the target species, and on the parameters evaluated. The phytochemical screening of all extracts was also evaluated and allowed the identification of anthraquinones, triterpenoids, saponins, and tannins. The composition of extracts differed between the seasons for all species. This study highlights the importance of considering savanna seasonality when studying the phytotoxicity of the species of this biome.
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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".