Streamlining Global Germplasm Exchange: Integrating Scientific Rigor and Common Sense to Exclude Phantom Agents from Regulation
Bibliographic record
Abstract
This collaborative work by over 180 researchers from 40+ countries addresses the challenges posed by "phantom agents"-putative pathogenic agents named in literature without supporting data on their existence. Those agents remain on regulatory lists, creating barriers in trade and plant certification. Historically identified based solely on symptoms, these agents lack isolates or sequence data, making reliable detection or risk assessment impossible. After reviewing over 120 such agents across 10 key plant genera, we recommend their removal from regulatory lists and call for revised standards aligned with modern diagnostics. This effort seeks to streamline germplasm exchange, benefiting global agriculture by removing the constraints imposed by phantoms.
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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 | 0.336 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".