Occurrence of <i>Pyrenophora tritici-repentis</i> causing tan spot in Argentina.
Bibliographic record
Abstract
Wheat (Triticum aestivum L.) is currently considered as one of the most important crops worldwide. It can be affected by several diseases. However, only a limited number of them, like 'tan spot' resulting from the fungus produced by Pyrenophora tritici-repentis, cause serious problems to the crop and may be given special attention. Tan spot has significant economic consequences. In recent years, the incidence of the disease has increased in many areas where wheat is cultivated, becoming a serious problem by causing losses of up to 70%. It has been found in a lot of countries worldwide: North Dakota, Nebraska and Kansas (USA), Canada, Australia, Asia, Pakistan, Czech Republic, Poland, Ukraine, Hungary, France, Denmark and Belgium. This disease has increased its incidence, prevalence and severity, particularly in the whole of the South Cone region in the last few years: Argentina, Brazil, Bolivia, Colombia, Ecuador, Peru, Paraguay and Uruguay. Tan spot is one of the most destructive and widespread problems of wheat production in Argentina. In this chapter, we summarize the knowledge of many and diverse contributions and we highlight what is known and unknown about the disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".