Data challenges for future plant gene editing: expert opinion
Bibliographic record
Abstract
Agricultural data in its multiple forms are ubiquitous. With progress in crop and input monitoring systems and price reductions over the past decade, data are now being captured at an unprecedented rate. Once compiled, organized and analyzed, these data are capable of providing valuable insights into much of the agri-food supply chain. While much of the focus is on precision farming, agricultural data applications coupled with gene editing tools hold the potential to enhance crop performance and global food security. Yet, digitization of agriculture is a double-edged sword as it comes with inherent security and privacy quandaries. Infrastructure, policies, and practices to better harness the value of data are still lacking. This article reports expert opinions about the potential challenges regarding the use of data relevant to the development and approval of new crop traits as well as mechanisms employed to manage and protect data. While data could be of great value, issues of intellectual property and accessibility surround many of its forms. The key finding of this research is that surveyed experts optimistically report that by 2030, the synergy of computing power and genome editing could have profound effects on the global agri-food system, but that the European Union may not participate fully in this transformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".