Contamination of a calcareous soil by battery industry wastes. I. Characterization
Bibliographic record
Abstract
A study of soil contamination due to the disposal of waste from a battery industry was conducted. The soil particle size, organic matter content, and buffering capacity were characterized. The heavy metal content of the soil was characterized with soil depth, soil particle size, and with respect to the fraction of the soil by which it was retained. Lead was found to be the dominant contaminant with all other metals present at considerably lower concentrations. Most of the lead was retained in the fraction of the soil that had a particle size less than 2 mm. This fraction represented 40.8% of the soil and contained 24 600 mg Pb/kg of soil. A particle size analysis indicated that 45.3% of soil particles were found to be greater than 4.75 mm. The pH of the contaminated soil in water was found to be 7.6 and was similar to the background soil. The similarity in pH was attributed to the high calcium content of the native soil. The lead content in the native soil that was collected 100 m away from the contaminated site was found to be 1967 mg/kg in the soil with particle sizes less than 2 mm (contaminated soil). The difference in pH between KCl solution (pH 7.0) and in water was found to be 0.6 indicating that the pH value was above the point of zero salt effect. An evaluation of the buffering capacity revealed that 297 mL of 0.5 M HNO3 per kg of soil was required to substantially modify the soil pH. The heavy metals in the soil were sequentially extracted to quantify the water soluble, exchangeable, carbonate, oxides, organic matter, and residual fractions. The Pb concentrations were mainly found in the carbonate and oxide fractions of the soil.Key words: heavy metals, soil pollution, characterization, retention form.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".