Effect of Diseases on Soybean Yield in the Top Eight Producing Countries in 2006
Bibliographic record
Abstract
The objective of this project was to compile estimates of yield loss in soybean [Glycine max (L.) Merr] to diseases in the top eight soybean-producing countries in 2006. The purpose was to provide information needed by local and world agencies to allocate funds for research and to help scientists focus and coordinate research efforts. Methods used by plant pathologists to estimate yield loss to diseases in these countries included systematic field surveys, cultivar trials, diagnostic clinic records, personal observations, and questionnaires sent to crop consultants and extension staff. The 2006 harvest of soybeans in the top eight soybean-producing countries was reduced an estimated 59.9 million metric tonnes (t) by diseases according to results of the current study. Soybean rust, caused by Phakopsora pachyrhizi, reduced yield in all these countries except Canada in 2006, and the total was more than any other. Next in decreasing order of total yield loss were soybean cyst nematode, brown spot, seedling diseases, anthracnose, and charcoal rot. Accepted for publication 27 October 2009. Published 25 January 2010.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".