SODplex, a Series of Hierarchical Multiplexed Real-Time PCR Assays for the Detection and Lineage Identification of <i>Phytophthora ramorum</i>, the Causal Agent of Sudden Oak Death and Sudden Larch Death
Bibliographic record
Abstract
Since its emergence in the 1990s, the invasive pathogen Phytophthora ramorum has spread in Europe and the west coast of North America, causing sudden oak death in the United States and sudden larch death in the United Kingdom, resulting in the mortality or destruction of millions of trees. Due to its invasive nature, its damage potential, its wide host range, and its ability to disseminate via the plant trade, P. ramorum has been placed on quarantine lists worldwide. Rapid and reliable detection of the pathogen and identification of its lineages are crucial to limit spread and inform mitigation and eradication efforts. SODplex, a suite of new multiplex real-time PCR tools, was developed to streamline the detection and identification of P. ramorum. It offers four multiplexed assays covering different use cases. SODplex-base combines primers and probes for the sensitive and accurate detection of Phytophthora spp. and P. ramorum. SODplex-ITS and SODplex-mito offer a single-step identification of P. ramorum and the EU1, NA1, and NA2 lineages present in the United States and Canada. SODplex-lin targets each of the four P. ramorum lineages present in Europe and North America in a single reaction. The assays have high levels of accuracy and are robust to the use of different instruments, different operators, and different temperatures. The redundancy within the assays reduces the likelihood of false negatives and false positives. The SODplex assays presented here improve the toolbox available for the detection of P. ramorum and its lineages. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".