Improved detection and identification of the sudden oak death pathogen <i>Phytophthora ramorum</i> and the Port Orford cedar root pathogen <i>Phytophthora lateralis</i>
Bibliographic record
Abstract
Early detection provides the best way to prevent introduction and establishment of alien plant pathogens. Amplification of DNA by PCR has revolutionized the detection and monitoring of plant pathogens. Most of those assays rely on the amplification of a fraction of the genome of the targeted species. With the availability of whole genomes for a growing number of fungi and oomycetes it is becoming possible to compare genomes and discover regions that are unique to a target organism. This study has applied this pipeline to develop a set of hierarchical TaqMan real‐time PCR detection assays targeting DNA of all four Phytophthora ramorum lineages, and a closely related species, P. lateralis . Nine assays were generated: three targeting DNA of all P. ramorum lineages, one for each lineage of P. ramorum , one for P. lateralis and one targeting DNA of P. ramorum and P. lateralis . These assays were very accurate and sensitive, ranging from 98.7% to 100% detection accuracy of 2–10 gene copies of the targeted taxa from pure cultures or inoculated tissues. This level of sensitivity is within the lowest theoretical limit of detection of DNA . It is expected that these assays will be useful because of their high level of specificity and the ease with which they can be multiplexed because of the inherent flexibility in primer and probe design afforded by their lack of conservation in non‐target species.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".