Field‐evolved resistance to Bt maize in sugarcane borer (<i>Diatraea saccharalis</i>) in Argentina
Bibliographic record
Abstract
BACKGROUND: Maize technologies expressing Bacillus thuringiensis (Bt) insecticidal proteins are widely used in Argentina to control sugarcane borer (Diatraea saccharalis Fabricius). Unexpected D. saccharalis damage was observed to Bt maize events TC1507 (expressing Cry1F) and MON 89034 × MON 88017 (expressing Cry1A.105 and Cry2Ab2) in an isolated area of San Luis Province. Diatraea saccharalis larvae were sampled from MON 89034 × MON 88017 fields in the area to generate a resistant strain (RR), which was subsequently characterized in plant and diet bioassays. RESULTS: Survivorship of the RR strain was high on TC1507 leaf tissue, intermediate on MON 89034 × MON 88017, and low on MON 810 (expressing Cry1Ab). The RR strain had high resistance to Cry1A.105 (186.74-fold) and no resistance to Cry2Ab2 in diet bioassays. These results indicate resistance to Cry1F and Cry1A.105 (and likely cross-resistance between them) but not to Cry1Ab or Cry2Ab2. Resistance to MON 89034 × MON 88017 was functionally recessive. Reviews of grower records suggest that resistance initially evolved to Cry1F, conferring cross-resistance to Cry1A.105, with low refuge compliance as the primary cause. A mitigation plan was implemented in San Luis that included technology rotation, field monitoring, and grower education on best management practices (BMPs) including refuges. CONCLUSION: In the affected area, the resistance to Cry1F and Cry1A.105 is being managed effectively through use of MON 89034 × MON 88017 and MON 810 in combination with BMPs, and no spread of resistance to other regions has been observed. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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".