Genome sequences of two Phytophthora species responsible for Sudden Oak Death and Soybean Root Rot provide novel insights into their evolutionary origins and mechanisms of pathogenesis
Bibliographic record
Abstract
The approximately 60 species of Phytophthora are all destructive pathogens, causing rots of roots, stems, leaves and fruits of a wide range of agriculturally and ornamentally important plants (1). Some species, such as P. cinnamomi, P. parasitica and P. cactorum, each attack hundreds of different plant host species, whereas others are more restricted. Some of the crops where Phytophthora infections cause the greatest financial losses include potato, soybean, tomato, alfalfa, tobacco, peppers, cucurbits, pineapple, strawberry, raspberry and a wide range of perennial tree crops, especially citrus, avocado, almonds, walnuts, apples and cocoa, and they also heavily affect the ornamental, nursery and forestry industries. The economic damage overall to crops in the United States by Phytophthora species is estimated in the tens of billions of dollars, including the costs of control measures, and worldwide it is many times this amount (1). In the northern midwest of the U.S., P. sojae causes $200 million in annual losses to soybean alone, and worldwide causes around $1-2 billion in losses per year. P. infestans infections resulted in the Irish potato famine last century and continues to be a difficult and worsening problem for potato and tomato growers worldwide, with worldwide costs estimated at $5 billion per year.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".