The many‐faced Janus of plant breeding
Bibliographic record
Abstract
Societal Impact Statement Plant breeding is crucial for improving agricultural crops for human use. However, an urgent rethink is needed to ensure the next generation of plant breeders have the necessary breadth of skills to provide ever more efficient, nutritious, profitable, and environmentally sustainable crops. Plant breeding is a multifaceted endeavor, which intersects with many other disciplines and professions. To help ensure that future plant breeding efforts are sustainable and relevant to the needs of society, it is vital that the interdisciplinary nature of the plant breeding profession is adequately reflected in student training and development. Summary Breeders need to have many faces to understand not only genetics but also environmental, social, and economic factors that are relevant for maintaining or improving crops for human use. In the United States, there is a long history of public involvement in agriculture and plant breeding. However, recent changes in the social systems underpinning public agriculture (i.e., funding structure) necessitate a rethinking of how agriculture education, specifically plant breeding education, should be facilitated. To provide viable plant breeding programs, it is necessary to explicitly acknowledge that breeding has been an interdisciplinary, long‐standing public endeavor to increase food system stability. Acknowledging this complexity has important pedagogical implications: the core of plant breeding resides in genetics, but the changing nature of this profession requires breeders to embrace a much broader training. Here, we suggest specific curricular objectives for plant breeders.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".