Inoculum Density on Heterodera glycines Development in Resistant and Susceptible Soybean Cultivars
Bibliographic record
Abstract
A series of factors can affect populations of H. glycines and even its life cycle, including inoculum density and genetic resistance of soybean cultivars. This study evaluated whether resistance reaction to H. glycines is effective in reducing nematode development under high inoculum concentration, as well as if such resistance reaction and inoculum density affect juvenil penetration and survival rate of H. glycines. Two trials were done using three soybean cultivars: one susceptible (BRS Valiosa RR) and two resistant (BRSGO Chapadões and BRSGO 8860RR) to H. glycines. The cultivars were subjected to four inoculum density (1,000, 2,500, 5,000 and 10,000 eggs and J2 per pot). The experimental design was completely randomized, in a 3 × 4 factorial scheme, with twelve replications. Two evaluations were done at 10 and 30 days after inoculation (DAI). Juvenile penetration in the roots was evaluated at 10 DAI and the number of females in the roots was estimated at 30 DAI. The survival rate was determined using both evaluations. Increasing initial density of H. glycines inoculum resulted in the increase of nematode final population in the susceptible cultivar, and the resistance reaction of soybean cultivars was not affected by the inoculum concentration. Penetration of J2 in the roots increased as inoculum density increased regardless of cultivar resistance or susceptibility. Nematode survival rate was greater in the susceptible cultivar.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".