Formation of Seedlings of Coffea canephora Pierre ex Froehner and Weed Control Under Application of Herbicides Oxyfluorfen and Pendimethalin
Bibliographic record
Abstract
One of the most relevant factors for the formation of coffee crops is to use quality seedlings. However, the competition of weeds for nutrients and water from the soil can negatively affect your obtaining. Thus, the control of weeds in nursery is often dependent on the use of herbicides, considering that the manual activity is costly. In this way, this work aimed to evaluate the effectiveness of weed control and the effects on the development of clonal seedlings of coffee (Coffea canephora Pierre ex Froehner) by application of herbicides oxyfluorfen and pendimenthalin in nursery conditions. The experiment was conducted under a completely randomized design in factorial scheme 2 × 5, two herbicides: Pendimethalin and Oxifluorfen in five doses: corresponding to 0, 1, 2, 4, 8 L.ha-1 of commercial products. Was evaluated during the experiment the emergence of weeds and to end (140 days), were evaluated: seedling Height, stem diameter, leaf area, number of sheets, number of roots, root length, root volume, dry mass of roots and shoot dry matter. There was significant effect for seedling height, shoot dry matter and total dry mass, in which the pendimethalin caused damage to seedlings in comparison to oxyfluorfen. The use of the herbicides Pendimethalin and Oxyfluorfen obtained satisfactory control of weeds with application of 1080g and 648 g a.i. ha-1, respectively.
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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) | 0.001 | 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".