EFFECT OF MINIMUM INPUTS OF INTEGRATED WEED CONTROL PROGRAMS IN WHEAT PRODUCTION UNDER NEWLY RECLAIMED SOIL CONDITIONS
Bibliographic record
Abstract
Two field experiments were conducted at Salah Al-Abd village, El-Bostan region, El-Behera Governorate, Egypt during 2005Egypt during /2006Egypt during and 2006Egypt during /2007 seasons, to study the effect of weed control treatments (Harmony Extra herbicide (Thifensulfuron), applied at the rates of 24 and 18 g/fed., hand weeding (twice) and unweeding (check), seeding systems (drilling and broadcasting, with recommended rate and 150 % of the recommended seeding rates for each method) and two nitrogen fertilizer levels (100 and 75 kg N/fed.) on the productivity of wheat under the conditions of new lands.Results indicate that using Harmony Extra herbicide at both rates was significant in reducing fresh weight of broadleaved weeds as well the significant increase in both of the number of kernels /spike, the number of spikes /m 2 and thus increase grain yield, from the back of resistance recommended to use the lower rate of this herbicide.There was significant effect for seeding system on fresh weight of broad leaved weeds, where it was noted that increasing seeding rate of wheat crop reduced fresh weight of broad leaved weeds as well as had a reduction in the number of kernels /spike, but it had an increase in number of spikes /m 2 thus, grain yield did not significantly affected.Decreasing nitrogen fertilizer levels caused significant reduction in fresh weight of broad leaved weeds, leading to a reduction in the number of spikes /m 2 , which was reflected on the reduction in grain yield.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".