Application of rock dust for the management of <i>Meloidogyne javanica</i> in soybean
Bibliographic record
Abstract
This study aimed to assess the effects of rock dust on Meloidogyne javanica in soybean grown in autoclaved and non-autoclaved soil and investigate the impacts on plant foliar nutrition. The nematode reproduction experiment was conducted in two periods (Trials 1 and 2) in a 5 × 2 factorial design (rock dust rates of 0, 1000, 2000, 3000, and 4000 kg ha−1 × autoclaved and non-autoclaved soil). Soybean were inoculated with 2000 eggs + second-stage juveniles (J2) of M. javanica, and nematode and vegetative variables were evaluated at 60 days after inoculation (DAI). The penetration experiment followed a 2 × 2 factorial design (rock dust rates of 0 and 2500 kg ha−1 × autoclaved and non-autoclaved soil). Soybean were inoculated with 2000 eggs + J2 of M. javanica and evaluated at 5, 10, 15, 20, and 25 DAI. The shoot dry biomass from the reproduction experiment (Trial 1) was analyzed for macro- and micronutrients. In Trial 1, nematode reproduction decreased proportionally to the increase in rock dust rate in non-autoclaved soil. In Trial 2, the lowest nematode numbers were predicted to be achieved with rates of 2213 to 2309 kg ha−1, and nematode numbers were lower in autoclaved than in non-autoclaved soil. Rock dust reduced J2 penetration at 10 and 15 DAI, and there was no formation of females in treated plants. Rock dust application increased foliar levels of Fe, B, and Cu. Rock dust reduced M. javanica reproduction, penetration, and development in soybean and promoted gains in Fe, Cu, and B foliar contents.
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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 | 0.000 |
| 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".