Biomass Production and Mineral Nutrient Accumulation by Weeds and Sweet Orange Trees in the Amazonian
Bibliographic record
Abstract
Accumulation of biomass and competition for nutrients can be used as parameters to identify species with higher potential for competition and, thus, with larger interference in crops. Consequently, studies addressing these parameters are important to weed science since they are the main factors that negatively affect the growth and productivity of cultivated plants. The objective of this research was to identify the species of weed plants with the largest potential of biomass production and accumulation of mineral nutrients in their leaves, which in turn, lead to higher interference in orange crops. In the floristic survey, 30 species of weed plants were identified and 14 botanic families, totaling 1341 sampled specimens. The phytosociological analysis showed, as per the importance value (IV), that the most representative weed species in the study area were as follows: Conyza bonariensis (L. (Cronquist)), Spermacoce latifolia Albl., Paspalum conjugatum PJ Bergius, Pueraria phaseoloides (Roxb.) Benth., Mollugo verticillate L., Peperomia pelucida (L.) Kunth, Euphorbia heterophylla L., Paspalum multicaule Poir and Waltheria corchorifolia Pers. Among these, the species with the highest production of biomass and accumulation of mineral nutrients in their tissues were S. latifolia, P. phaseoloides, P. conjugatum and C. bonariensis. This result suggests that these species are of high competitive potential against orange crops due to their high capacity for biomass and micronutrients accumulation.
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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 | 0.001 | 0.001 |
| 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".