Facilitating the adoption of high‐throughput sequencing technologies as a plant pest diagnostic test in laboratories: A step‐by‐step description
Bibliographic record
Abstract
Abstract High‐throughput sequencing (HTS) is a powerful tool that enables the simultaneous detection and potential identification of any organisms present in a sample. The growing interest in the application of HTS technologies for routine diagnostics in plant health laboratories is triggering the development of guidelines on how to prepare laboratories for performing HTS testing. This paper describes general and technical recommendations to guide laboratories through the complex process of preparing a laboratory for HTS tests within existing quality assurance systems. From nucleic acid extractions to data analysis and interpretation, all of the steps are covered to ensure reliable and reproducible results. These guidelines are relevant for the detection and identification of any plant pest (e.g. arthropods, bacteria, fungi, nematodes, invasive plants or weeds, protozoa, viroids, viruses), and from any type of matrix (e.g. pure microbial culture, plant tissue, soil, water), regardless of the HTS technology (e.g. amplicon sequencing, shotgun sequencing) and of the application (e.g. surveillance programme, phytosanitary certification, quarantine, import control). These guidelines are written in general terms to facilitate the adoption of HTS technologies in plant pest routine diagnostics and enable broader application in all plant health fields, including research. A glossary of relevant terms is provided among the Supplementary Material.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.028 |
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".