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 Kinetics, as a fundamental requirement of nearly all industrial activities and engineering researches, plays a great role in leaching processes. Although there are many pieces of research on its application, there is not a clear pathway for investigating the kinetics of leaching and researchers usually follow different strategies in their studies. The conventional investigation techniques, which usually do not consider the mixed mechanisms and possibility of any change in the mechanism, normally include many calculations, plots, and inadequate capabilities to detect changes in the controlling mechanism of leaching. In this review, the main mathematical models of leaching and all possible scenarios are presented and discussed. The effect of various leaching parameters (including leaching agent, temperature, particle size, agitation, and solid to liquid ratio) on the rate of dissolution is summarized. Besides, two main approaches of rate determination step (single controlling mechanism and combined resistances method) are described and compared by reporting related equations and suitable examples. A technique to detect any changes in the leaching controlling mechanism is introduced and the alternatives to confirm the results are described. Additional models and equations were suggested for the cases that there is no agreement between data and the conventional models. Also, situations which are ignored in simple models (e.g., reversibility of the leaching reactions, adsorption and desorption of leached species, influence of charge and surface potential, existence of multiple reactants in the solid, galvanic effect, wide particle size distribution, etc.) to develop more legalistic models are discussed. Considering various possible mechanisms in the kinetics of leaching, equations are derived for industrial leaching reactors.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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