Transgenic Insecticidal Corn: The Agronomic and Ecological Rationale for Its Use
Bibliographic record
Abstract
We agree with Obrycki et al. (2001) that a broad-based ecological approach for new pest management technologies is desirable, but we unanimously and strongly disagree with some of their assumptions and conclusions about Bt corn. Bt corn is corn that has been genetically engineered to produce insecticidal proteins from the bacterium Bacillus thuringiensis. Because Bt corn is important for effective and ecologically sound management of lepidopteran pests of corn, we provide here relevant data, some of which is new, to help clarify the issues raised by Obrycki et al. (2001). Obrycki et al. (2001), citing Barry and Darrah (1991), claim that traditional plant breeding has developed corn plants that adequately protect against European corn borer. However, Barry and Darrah (1991) reported only “some resistance to whorl leaf feeding…·[or] some resistance to sheath and sheath collar feeding,” which is not comparable with the nearly complete protection provided by Bt corn. Carpenter and Gianessi (2001) estimated that, nationally, during “10 of the 13 years between 1986 and 1998, European corn borer infestations…·were such that corn growers would have realized a gain from planting Bt corn.” Similarly, the Environmental Protection Agency (USEPA 2000) estimated a net benefit to growers of $$8.18 per hectare on 8 million hectares of Bt corn planted in 1999, or a national benefit of $65.4 million (USEPA 2000). Even considering the inherent year-to-year variability in pest population density, the EPA estimated the annual benefit to corn growers at $38–$219 million (USEPA 2001).
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".