Combined effects of <i>Ascophyllum nodosum</i> seaweed extract and biological control agents on <i>Meloidogyne javanica</i> in soybean
Bibliographic record
Abstract
This study aimed to investigate the efficacy of Ascophyllum nodosum extract, in combination or not with biocontrol agents, in the management of Meloidogyne javanica in soybean, assess treatment effects on soil biological activity, and determine the total phenolic content of the extract. The experiment was conducted according to a 5 × 2 factorial design, with three biocontrol agents (Bacillus methylotrophicus, Pochonia chlamydosporia, and Trichoderma harzianum), a chemical nematicide (abamectin, positive control), and an untreated (negative) control, each in the presence and absence of A. nodosum extract. Treatments included six replications. Nematode inoculation (2000 eggs + juveniles of M. javanica per plant) was performed at the time of sowing. Sixty days later, plants were evaluated for height, shoot weights, numbers of M. javanica eggs and J2 extracted from roots, and soil biological activity. Biological and chemical nematicide treatments were effective in reducing M. javanica multiplication. There was a synergic effect of A. nodosum extract on M. javanica multiplication for the treatments with A. nodosum + T. harzianum, A. nodosum + abamectin (Trial 1) and A. nodosum + P. chlamydosporia (Trial 2). B. methylotrophicus and T. harzianum treatments enhanced plant development and soil microbial biomass carbon. Higher values of soil basal respiration and metabolic quotient (qCO2) were observed in the A. nodosum + abamectin treatment. A. nodosum extracts at 75 g L−1 had a total phenolic content of 13 255 and 11 608 mg GAE 100−1 g in Trials 1 and 2, respectively.
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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".