Biosorption of Cr(VI) from aqueous solution using agricultural wastes, with artificial intelligence approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it