Twenty five years of private wheat breeding in the UK: Lessons for other countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Crop research sectors in many countries are facing reduced public support with public breeding programs being gradually replaced by private ones. This paper explores the UK experience with the privatization of wheat breeding that began in 1987. The analysis presented in this paper is based on interviews with sixteen experts currently involved in wheat research breeding in the UK. Taking a snapshot of UK wheat research today, it would be easy to conclude that the UK sector made a smooth transition from public to private breeding. However, this is not the case. The UK faced many challenges in establishing an integrated wheat innovation system and has only recently developed policies and funding processes that have enabled upstream public scientists to work with private wheat breeding industry. As policy makers around the world contemplate the privatization of crop breeding, important lessons can be drawn from the UK crop research funding model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it