Herbicide on Weed Composition, Diversity and Density in Silage Corn (cv. Sc 704)
Bibliographic record
Abstract
In order to study the effect of plant density, planting pattern and herbicide dosage of nicosulfuron, a field experiment was arranged in a factorial split plot treatments based on RCBD with three replications in Chenaran, Khorasan Razavi, in 2010. The experimental treatments consisted of a factorial plant density (100000, 120000 and 140000 plants ha-1) in the planting pattern (single and double row) as main plot and herbicide dosage of nicousulforon in four levels (0, 1, 1/5 and 2, l.ha-1) as sub-plot. Samplings were made at in five stages (37days after the emergence of corn and it was repeated once per 20 days). The results indicated reducing the weed density and dry matter of weeds in the first stage after the herbicide treatment. Moreover, it was observed a significant interaction effect between plant density with planting pattern and between planting pattern with herbicides dosages during growth season on reducing weed density and dry matter. Also results indicated that in between of this experiment's treatments, nicosulfuron herbicide reduced weed density at the beginning of growth season and double row planting pattern suppressed weed density during growing season, and resulted in lowest Jacard similarity index (Sj) of weed species. Results also indicated that with increasing of plant density and herbicide dosage especially in composition of double row planting pattern, according to Shannon- Wiener index, sensitive population such as common purslane (Portulaca oleracea L.), buckhorn plantain (Plantago lanceolata L.), prostrate knotweed (Polygonum aviculareL.), black nightshade (Solanum nigrum L.) and Johnson grass (Sorghum halepens L.) was reduced in during growing season. Simpson dominance index, showed that some low populated weeds such as redroot pigweed (Amaranthus retroflexus L.), common lambsquarters (Chenopodium album L.), field bindweed (Convolvulus arvensis L.) and Canada thistle (Circum arvensis L.) persisted their growth up to the end of growing season.
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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".