Comparative studies of zinc, cadmium, lead and copper on economically viable adsorbents
Bibliographic record
Abstract
Lead and cadmium are very important metals even at trace levels because many health hazards are associated with them. Whereas, zinc and copper are toxic only when these are present at higher concentrations. Their removal from the contaminated samples is of utmost importance. The technique of adsorption using various economically viable adsorbents such as bagasse, bottom ash, rice husk ash, sawdust, and used tea leaves has been applied for their removal from aqueous solutions. Various parameters such as contact time, adsorbent dose, and metal concentrations were studied, optimized and applied to the present study. The equilibrium data obtained were analyzed in the light of Freundlich and Langmuir isotherms. Results revealed that rice husk ash is most efficient in removing lead and copper from aqueous solutions in comparison to the other adsorbents. Whereas in the case of cadmium, bottom ash was found to be of maximum efficiency. Bagasse was of maximum efficiency in removing zinc.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".