Mechanisms Involved in Soybean Rust‐Induced Yield Reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soybean rust (SBR; caused by Phakopsora pachyrhizi Syd. and P. Syd.) leads to premature leaf loss and yield reduction. The objectives of this study were to assess effects of SBR infection on soybean [ Glycine max (L.) Merrill] yield and to identify causes for the yield reduction. Experiments were conducted in the 2005–2006 and 2006–2007 growing seasons at Londrina, Brazil. The five treatments were SBR infection beginning at either (i) the R2 or (ii) R5 growth stages; nondiseased defoliation treatments to mimic the leaf loss when SBR started at either (iii) the R2 or (iv) R5 growth stages; and (v) a disease‐free, nondefoliated control. The control and defoliation treatments were protected against SBR by fungicide applications. Disease severity, lesion area, and leaf area were monitored from R2 to R7. Biomass and seed yield were measured at maturity. Mean SBR‐induced yield reductions were 67% when infection started at R2 and 37% when infection started at R5. Leaf loss alone reduced yield significantly in only one year and only when defoliation treatments were begun at R2 (31% in 2005–2006). Soybean rust–induced yield loss was attributable to (i) premature leaf loss, (ii) reduction in canopy green leaf area due to SBR lesions, (iii) reduction in dry matter accumulation per unit absorbed radiation by the nonlesion green leaf area, and (iv) reduction in harvest index. The response of harvest index was attributable to reduced seed set and seed mass resulting likely from SBR‐induced reductions in rate of dry matter accumulation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 it