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
Abstract Liquid–liquid extraction, also known as solvent extracting, is a well‐established separation technique that depends on the unequal distribution of a solute between two immiscible liquids. The initial feed liquid containing the solute is brought into contact with a solvent that is selected to have a greater affinity for the solute. The partition of the solute can be enhanced by adding a chemical extractant to the solvent; this practice is widespread in the hydrometallurgical and nuclear industries. Most industrial extractors operate continuously with countercurrent flow of the two phases. In mixer–settlers, the phases are contacted as a well‐agitated dispersion of drops, which are then sent to settling tanks for phase disengagement. In extraction columns, the dispersed drops move countercurrently against the flow of the second (continuous) phase. The physics, chemistry, and practice of extraction, with brief descriptions of important industrial extraction processes and equipment, are presented. Research on hydrodynamic aspects of process design, eg, axial mixing, drop dispersion, and coalescence, is reviewed. New extraction techniques, eg, membrane extraction, supercritical exctraction, and two‐phase aqueous extraction, are discussed.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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