Bibliographic record
Abstract
Disease is one of the limiting factors for the production of sainfoin(Onobrychis viciaefolia).By the end of 2011,32 diseases had been found in this legume forage world wide,including 27 fungal diseases,2 bacterial diseases,1 virus disease and 2 nematode diseases.Of the fungal diseases,24 were found in China,9 in Britain,5 in Iran,3 in Turkey,2 in Canada,and 1 in each of the former Soviet Union and Germany.Among these diseases,13 such as leaf spot(Cercospora sp.),anthracnose(Colletotrichum truncatum)and root rot(Aphanomyces euteiches)only occurred in China,whereas 3 diseases,powdery mildew(Erysiphe trifolii),ring spot(Pleospora herbarum)and root rot(Phytophthora citricola,P.cryptogea,P.megasperma)only occurred abroad.In total,there were 36 fungal species pathogenic on the plant.Of the plant tissues damaged,21 were found in leaves and stems,5 in root systems,and 1 which can cause systematic infection in the whole plant.In China,20 were found in Gansu,9 in Xinjiang,5 in Inner Mongolia and fewer in other provinces.These bacteria,virus and nematode diseases occurred abroad except for stem epidemic disease(Pseudomonas syingae).Up to now,the loss,life cycle and management of some frequently occurring stem-leaf diseases such as powdery mildew,rust(Uromyces onobrychis),black rot(Alternaria tenuis)have been studied at various levels but there have been few studies on most of the stem-leaf diseases,root diseases and systematic diseases.Therefore,it is necessary to focus on several important diseases and to accurately identify their causal agents,frequently survey their dynamics and clearly determine their occurrence.The aim of this review is to propose effective management strategies for farmers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".