Pilot Study: Impact of Biochar Derived from Activated Sludge with Pseudomonas putida on Cherry Tomato Cultivation
Bibliographic record
Abstract
Biochar produced from activated sludge boosts soil fertility by supplying nutrients, improving water retention, and optimizing nutrient accessibility.Its durable carbon composition sequesters carbon in the soil for long-term storage.Reduces greenhouse gases such as CH4 and N2O by modifying microbial dynamics and enhancing soil aeration.Furthermore, biochar captures pollutants, minimizing environmental hazards and promoting sustainable farming practices.This study investigates the impact of biochar derived from activated sludge and biochar loaded with Pseudomonas putida on cherry tomato growth.Biochar was produced from activated sludge at the Babel wastewater treatment plant, with Pseudomonas putida isolated from the same source.In a 90-day pot experiment, four biochar treatments were tested: two concentrations (1% and 5%) and the same concentrations loaded with Pseudomonas putida.Results showed that 5% biochar loaded with Pseudomonas putida significantly enhanced cherry tomato growth, with the highest fresh shoot weight (179.9 g) and chlorophyll content (62.22 SPAD), All biochar treatments significantly enhanced soil chemical properties, such pH, electrical conductivity, and level of phosphorus, carbon, and nitrogen, leading to enhance plant growth and productivity (P < 0.0034) compared to the control (P < 0.0045).Scanning Electron Microscopy (SEM) analysis revealed a reduction in biochar particle diameter from 33.95 nm before pyrolysis to 16.17 nm after pyrolysis.These findings suggest that 5% biochar loaded with Pseudomonas putida is effective for small-scale agricultural applications.
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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 | 0.000 | 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 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".