Content of Heavy Metals in Soil and in Pineapple Fertilized With Sewage Sludge
Bibliographic record
Abstract
The usage of sewage sludge in agriculture can increase the levels of heavy metals in the soil, compromising their use as fertilizer. The objective of this study was to evaluate the contamination of soil and pineapple by heavy metals after the application of treated sewage sludge by different forms in three orders of soils under greenhouse conditions. The treatments, in a factorial scheme 7 × 3 were distributed in a randomized complete block design with three replications, corresponding to seven fertilization management: soil without fertilization, chemical fertilization, fertilization with composted sludge sewage, fertilization with vermicompost sewage sludge, fertilization with solarized sewage sludge, fertilization as sewage sludge dried in a Bruthus-Albrecht rotary sludge dryer and fertilization with limed sludge sewage, combined with three orders of soils: Cambisol, Nitisol and Acrisol. The Zn, Cu, Cr, Pb, Ba, Cd, Ni, As and Se contents were analyzed in the soil, in the leaf of greater length (D leaf) and in the fruit pulp of the pineapple. The Acrisol provided more favorable conditions to the increase in the availability and absorption of Pb and As by D leaf and fruit of the pineapple. Fertilization with composted, vermicompost and solarized sewage sludge provided high levels of Pb, As, Zn in leaf D and in the pineapple fruit, relating the interference of sludge stabilization process with the absorption of metals by pineapple fruits.
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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".