Are There Adequate Incentives for Research and Innovation in the Plant Breeding Supply Chain?
Bibliographic record
Abstract
The breeding supply chain has expanded with genomic technology to include basic research scientists and applied genomicists along with traditional plant breeders and farmers. Genomicists have focused on identifying specific DNA sequences or quantitative trait loci (QTL) that can be used as molecular markers. However, the use of molecular marker-assisted selection (MAS) by breeders in their programs requires the identified QTL to be reliably correlated with agronomically desirable traits. Replication research is critical for reducing the risk associated with the adoption of new marker-based (or QTL-based) selection strategies, but the applied scientists doing genomics research often do not have the incentives to do replication and other research required to verify the reliability of markers. The misalignment of incentives in the breeding supply chain can curtail the development of the projected advances in food production by genomics research. Using a sample of 24 genomic journals, we found more highly ranked journals tend to favor new research on identifying new QTL over replication research on previously identified QTL. Given that breeders will tend to adopt only those markers perceived to be reliable, the implicit lack of incentives for basic and applied genomic scientists to undertake replication research can impede agricultural innovation.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".