One stop shop: backbones trees for important phytopathogenic genera: I (2014)
Bibliographic record
Abstract
Many fungi are pathogenic on plants and cause significant damage in agriculture and forestry. They are also part of the natural ecosystem and may play a role in regulating plant numbers/density. Morphological identification and analysis of plant pathogenic fungi, while important, is often hampered by the scarcity of discriminatory taxonomic characters and the endophytic or inconspicuous nature of these fungi. Molecular (DNA sequence) data for plant pathogenic fungi have emerged as key information for diagnostic and classification studies, although hampered in part by non-standard laboratory practices and analytical methods. To facilitate current and future research, this study provides phylogenetic synopses for 25 groups of plant pathogenic fungi in the Ascomycota, Basidiomycota, Mucormycotina (Fungi), and Oomycota, using recent molecular data, up-to-date names, and the latest taxonomic insights. Lineage-specific laboratory protocols together with advice on their application, as well as general observations, are also provided. We hope to maintain updated backbone trees of these fungal lineages over time and to publish them jointly as new data emerge. Researchers of plant pathogenic fungi not covered by the present study are invited to join this future effort. Bipolaris, Botryosphaeriaceae, Botryosphaeria, Botrytis, Choanephora, Colletotrichum, Curvularia, Diaporthe, Diplodia, Dothiorella, Fusarium, Gilbertella, Lasiodiplodia, Mucor, Neofusicoccum, Pestalotiopsis, Phyllosticta, Phytophthora, Puccinia, Pyrenophora, Pythium, Rhizopus, Stagonosporopsis, Ustilago and Verticillium are dealt with in this paper.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.033 |
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".