Unveiling the biostimulant assets of <i>Sargassum ilicifolium</i> with <i>Pseudomonas aeruginosa</i> to mitigate heavy metal stress in the sunflower as a sustainable approach
Bibliographic record
Abstract
Lead (Pb) and nickel (Ni) contaminations introduced to cultivable land by many sources that accumulate in the soil profiles and polluted water bodies, which reduces the growth and production of various crops. Sargassum ilicifolium extract recognized as a potential source of bioactive compounds having total organic carbon content (32.1%), nitrogen content (2.12%), carbon:nitrogen ratio (15.16), indole acetic acid (IAA) (6.0 µg/mL), total phenolic content (TPC) (7.01 mg%), and potent antioxidant activities DPPH (47.12% inhibition), ABTS (21.6% inhibition), and superoxide (90.45%), which reduces toxic heavy metal effect and improves soil fertility. Pseudomonas aeruginosa is characterized as plant growth-promoting rhizobacteria by in vitro testing of IAA (7.3 µg/mL), siderophore production, phosphate solubilization, cyanide production, ammonia production, and gelatin liquefaction to defend the plants by complete mineralization of toxic pollutants. In this research, P. aeruginosa showed a heavy metal biosorption capacity of 99.06% for Pb and 98.8% for Ni. Plant morphological traits were improved by the individual and combined use of P. aeruginosa and S. ilicifolium as a soil amendment under 0.5 mM Pb and Ni pollution. The biochemical assessment of antioxidant enzymes and growth-related compounds, i.e., DPPH-radical scavenging activity, superoxide dismutase, peroxidase, TPC, proline, malondialdehyde content, photosynthetic pigments, and EL, also showed that plants were more tolerant to heavy metal stress. The screen house and field trial correlation plot illustrated a positive association with growth parameters and defense markers (TPC, DPPH, antioxidant enzyme) under Pb and Ni toxicity. Further studies are required to examine the mechanistic pathways and long-term effects of these biostimulants.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 teacher head, 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".