Trait stacking for biotech crops: an essential consideration for agbiotech development projects for building trust
Bibliographic record
Abstract
The development of agricultural biotechnology humanitarian projects for food security in the last five years has been rapid in developing countries and is expected to rise sharply over the coming years. An extremely critical issue in these projects involves building trust with the community and farmers they aim to serve. For the first time, our social audit engagement with one of these initiatives, the Water Efficient Maize for Africa project, has revealed that a critical but unrecognized component of building trust with farmers involves publicly addressing the concerns surrounding stacked trait crops. As a result, we argue in this article that it is critical to actively anticipate the concerns that could be raised over trait stacking by incorporating them into global access plans of such initiatives early in order to facilitate adoption, provide the best value to the small-scale farmer and gain trust with the community whom these projects aim to serve. This perspective, based on an actual international social audit, should be of value to scientists, funders and partners involved in biotech development initiatives for food security.
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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 | 0.081 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".