The challenges of commercializing second-generation transgenic crop traits necessitate the development of international public sector research infrastructure
Bibliographic record
Abstract
It has been 30 years since the first transformation of a gene into a plant species, and since that time a number of biotechnology products have been developed, with the most important being insect- and herbicide-resistant crops. The development of second-generation products, including nutrient use efficiency and tolerance to important environmental stressors such as drought, has, up to this time, been less successful. This is in part due to the inherent complexities of these traits and in part due to limitations in research infrastructure necessary for public sector researchers to test their best ideas. Here we discuss lessons from previous work in the generation of the first-generation traits, as well as work from our labs and others on identifying genes for nitrogen use efficiency. We then describe some of the issues that have impeded rapid progress in this area. Finally, we propose the type of public sector organization that we feel is necessary to make advances in important second-generation traits such as nitrogen use efficiency.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".