An Overview of the Epidemiology of Phakopsora pachyrhizi in the State of Mato Grosso, Brazil
Bibliographic record
Abstract
This review seeks to expand the knowledge about the epidemiology of Asian sybean rust in the state of Mato Grosso and contribute to ensuring the economic sustainability of soybean crop. It is discussed the Phakopsora pachyrhizi potential of dispersal from Asia to South America and finally to Mato Grosso state. The origin of the Asian soybean rust inoculum within Mato Grosso is addressed by the survival in volunteer and soybean weed plants (Pitelli, 2015) in other crops such as cotton. Data on the adverse environmental effect on the soybean plants survival are shown mainly the water deficit from June to August. Reports on the effect air temperature and mainly solar radiation on the mortality of airborne spores during their anemophilous spread on sunny days are also discussed. This increase of knowledge aims to make the soybean-free period more efficient by the knowledge on the soybean plants survival and on the fungus viability in the month of August. Due to the proximity of soybean farms, during the soybean-free period, in other states (Tocantins, Goiás, Rondônia, etc.) and in other neighbor countries we discuss the likelihood that inoculum in the state may also originate in out-of-state crops during the Mato Grosso soybean-free period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".