Diversity, Composition and Population Dynamics of Arthropods in the Genetically Modified Soybeans Roundup Ready® RR1 (GT 40-3-2) and Intacta RR2 PRO® (MON87701 x MON89788)
Bibliographic record
Abstract
Knowledge of insect diversity is essential for ecological studies and pest management. The aim of this study was to study the occurrence, abundance of target and non-target pests in genetically modified insect resistant (Bt) and glyphosate-tolerant soybeans (RR1 and RR2), with and without the application of insecticides. Experiments were carried out in the agricultural year of 2011/2012 in four municipalities in the State of Mato Grosso do Sul. The treatments were: 1 - Roundup Ready® RR1 soybeans without insecticide application; 2 - Roundup Ready® RR1 soybeans with application whenever the control level was reached; 3 - Intacta RR2 PRO® soybeans without insecticide application; and 4 - Intacta RR2 PRO® soybeans with application whenever the control level was reached. The evaluations were initiated soon after emergence of the plants at weekly intervals. In order to obtain representative gradients of species composition, we used the method of ordering by non-metric multidimensional scaling (NMDS) and the Bray-Curtis dissimilarity index. Insects of the order Lepidoptera presented 7547 specimens, composing more than 70% of the insect community. The orders Coleoptera and Hemiptera also stood out, consisting of 2066 and 331 insects, respectively. Most of the samples were recorded in stages V8 to R2 for defoliating caterpillars and between R5.2 and R6 for the phytophagous stink bug complex. The Bt technology significantly reduced the target insect pests and favored populations of natural enemies. The treatments with insecticide application resulted in reduction of arthropods collected and changes in population outbreaks when compared to areas without spraying.
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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 | 0.001 | 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".