Growth Analysis of Sourgrass: Does Herbicide Resistance Affect Its Development?
Bibliographic record
Abstract
Sourgrass (Digitaria insularis) is highlighted as one of the most troublesome weeds in Brazilian agriculture. The growth analysis of the species and biotypes with resistance to glyphosate are preponderant to support management strategies. In this way, the aim of this work is to compare the growth of biotypes resistant and susceptible to glyphosate, and to characterize the species growth in field conditions. The greenhouse experiment was installed in randomized blocks design, in factorial scheme 2 × 10, with eight replications. Factor A comprised the biotypes, and factor B the fortnight evaluations. The dry mass of roots, stems, leaves and shoot were assessed, besides leaf area and plant height. From these variables, the relative growth rate, net assimilation rate and leaf area ratio were calculated. For the field experiment, the same variables were assessed and the same parameters were calculated, without distinction on resistant or susceptible biotype. The biotype with resistance to glyphosate did not show adaptative disadvantages compared to the susceptible one. In this way, it is necessary to prevent the entry of resistant biotypes in cropped fields, as once established the area may not naturally return to the initial frequency of susceptible biotypes. Sourgrass shown slow initial growth and dry mass accumulation up to 42 days after emergence, indicating that control of this specie should be performed preferably before this period.
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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".