Biosorption of Cr(VI) from aqueous solution using agricultural wastes, with artificial intelligence approach
Bibliographic record
Abstract
Removal of Cr(VI) from aqueous solution by date-palm-leaves (DPL) and broad-bean-shoots (BBS) was investigated. FTIR, SEM, and EDAX showed that DPL has higher ability for ion-exchange to remove Cr(VI). Langmuir and Freundlich adsorption isotherms and kinetics revealed that DPL exhibited higher biosorption capacity. At Cr(VI) 100 mg/L, biosorbent-dose 5 g/L and 60 min contact-time, maximum Cr(VI) removal for DPL (98%) and BBS (95%) was achieved at pH 2 and 1, respectively. Adaptive-neuro fuzzy inference system determined the most important factor affecting Cr(VI) removal. The model indicated that DPL is more tolerant to pH levels, while BBS is a pH-sensitive adsorbent.
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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) | 0.000 | 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".