تاثیر تلفیق تراکم بوته، آرایش کاشت وعلف کش نیکوسولفورون درکنترل علفهای هرز ذرت سیلویی سینگل کراس ۷۰۴
Bibliographic record
Abstract
In order to investigate the possibility of reducing herbicide usage in combination with plant density and planting pattern, a field experiment was designed in Chenaran, Khorasan Razavi, on 2010. The experiment was split plot treatments based on RCBD with three replications in factorial arragement. The treatments included of 3 level of plant density (100000, 120000 and 140000 plants ha-1) and 2 level of planting pattern (single and double row) as a main plot and Nicosulfuron dosage at four levels[(0), 50% recommended dose (1 l ha-1 ), 75% recommended dose (1.5 l ha-1) and 100% recommended dose (2 l ha-1)] as a sub-plots. The control plots consisted of weedy control and weed free check, respectively. Sampling was done to measure weed density, total dry matter and evaluating herbicide effect on weeds according to the EWRC 20 days after the corn emergence and repeated every 20 days. Corn silage yield was increased due to reduction of double row planting pattern and increasing of planting density significantly in dough seed stage. The results also showed that reduction of the weed density and total dry matter of weeds and desirable weed controls at first stage after the herbicide treatments. Experimental treatments had also a significant effect on weed species diversity according to shannon- winer index,sensitive population such as common purslane (Portulaca oleracea L.), buckhorn plantain(Plantago lanceolata L.), prostrate knotweed (Polygonum aviculare L.), black nightshade (Solanum nigrum L.) and Johnson grass (Sorghum halepens L.) was reduced and according to simpson diversity index, low population such as redroot pigweed (Amaranthus retroflexus L.),common lambsquarters (Chenopodium album L.), field bindweed (Convolvulus arvensis L.) and Canada thistle (Circum arvensis L.) by the end of growth 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".