Plant genetic resources for food and agriculture: opportunities and challenges emerging from the science and information technology revolution
Bibliographic record
Abstract
Contents Summary 1407 I. Introduction 1408 II. Technological advances and their utility for gene banks and breeding, and longer-term contributions to SDGs 1408 III. The challenges that must be overcome to realise emerging R&D opportunities 1410 IV. Renewed governance structures for PGR (and related big data) 1413 V. Access and benefit sharing and big data 1416 VI. Conclusion 1417 Acknowledgements 1417 ORCID 1417 References 1417 SUMMARY: Over the last decade, there has been an ongoing revolution in the exploration, manipulation and synthesis of biological systems, through the development of new technologies that generate, analyse and exploit big data. Users of Plant Genetic Resources (PGR) can potentially leverage these capacities to significantly increase the efficiency and effectiveness of their efforts to conserve, discover and utilise novel qualities in PGR, and help achieve the Sustainable Development Goals (SDGs). This review advances the discussion on these emerging opportunities and discusses how taking advantage of them will require data integration and synthesis across disciplinary, organisational and international boundaries, and the formation of multi-disciplinary, international partnerships. We explore some of the institutional and policy challenges that these efforts will face, particularly how these new technologies may influence the structure and role of research for sustainable development, ownership of resources, and access and benefit sharing. We discuss potential responses to political and institutional challenges, ranging from options for enhanced structure and governance of research discovery platforms to internationally brokered benefit-sharing agreements, and identify a set of broad principles that could guide the global community as it seeks or considers solutions.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".