Bibliographic record
Abstract
adsorption Green Adsorption 74-75 case study 89-90 definition 71-72 hydrophilic compounds 72 hydrophobic compounds 72-73 innovative applications of 88, 89 kinetic models 77-78 metal uptake mechanisms 78-79 polymer matrix 73-74 adsorption capacity definition 74 Green Adsorbents adsorbent dosage 76-77 co-ions effect 77 influence of pH 75-76 initial solute concentration 76 temperature 76 adsorptive 72, 74, 76 agricultural residue 12, 20, 74, 79, 80 agricultural waste 20, 21, 74, 75, 79-82 agro-residue waste 11, 17, 21, 22, 80 alkali fusion-leaching process 58 alkali leaching 50, 57-59, 61 aminopolycarboxylates 127, 128, 143, 145, 152 anthropocentrism 5 aprotic ionic liquids 169 aqueous phase volume fractions 223-224 artificial ore 32-33 atom efficiency 10, 11 Sustainable Metal Extraction from Waste Streams, First Edition.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.705 | 0.573 |
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".