Molecular Detection and Quantification of<i>Pythium</i>Species: Evolving Taxonomy, New Tools, and Challenges
Bibliographic record
Abstract
The genus Pythium is one of the most important groups of soilborne plant pathogens, present in almost every agricultural soil and attacking the roots of thousands of hosts, reducing crop yield and quality. Most species are generalists, necrotrophic pathogens that infect young juvenile tissue. In fact, Cook and Veseth have called Pythium the "common cold" of wheat, because of its chronic nature and ubiquitous distribution. Where Pythium spp. are the cause of seedling damping-off or emergence reduction, the causal agent can easily be identified based on symptoms and culturing. In more mature plants, however, infection by Pythium spp. is more difficult to diagnose, because of the nonspecific symptoms that could have abiotic causes such as nutrient deficiencies or be due to other root rotting pathogens. Molecular methods that can accurately identify and quantify this important group are needed for disease diagnosis and management recommendations and to better understand the epidemiology and ecology of this important group. The purpose of this article is to outline the current state-of-the-art in the detection and quantification of this important genus. In addition, we will introduce the reader to new changes in the taxonomy of this group.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".