Phytosociology and Behavior of Weeds in Maize as Influenced by Spatial Arrangements
Bibliographic record
Abstract
Using the right spatial arrangement is a sustainable way to prevent or at least delay the emergence of weeds in the crop production. This study evaluated the influence of row spacing and plant density of maize on weed control based on the phytosociological survey. It was conducted on an Oxisol textured medium in a 400 m2 area under semiarid conditions. The hybrid maize 30F53YH was managed under a no-till cropping system with three types of row spacing (0.35 m, 0.50 m, and 0.70 m) and three plant densities (5.0 plants m-2, 6.5 plants m-2, and 8.0 plants m-2). The experimental design was randomized complete blocks with four replications in a factorial arrangement 3 × 3. The phytosociological survey of the weeds was randomly performed four times in each subplot, using the inventory square (0.5 m × 0.5 m). The collected data were analyzed using the R statistical program. Among the specimen’s families identified on the field, three of them need to be highlighted due to its high values of density, frequency, and dominance. These families were Fabaceae, Poaceae, and Amaranthaceae. Also, it was identified that the Leucaena leucocephala species may be classified as a weed, as it acted as an invasive species on maize. The weed control was greatly influenced by the interaction of both parameters rather than only row spacing or the plant density factor. The results showed that the reduced spacing and high crop population decreased the presence of weeds in the maize crop.
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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) | 0.000 | 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".