Application of inverse gas chromatography to bench scale flotation of sulphide ore
Bibliographic record
Abstract
Inverse gas chromatography (IGC) is receiving increasing attention due to its high precision and ease of application in determining various characteristics of a wide variety of materials of different shapes and morphologies. This study details an experimental investigation into the application of the IGC technique to the flotation of sulphide minerals. Rather than giving the surface energy, the trends associated with the various concentrates and tailings are described. Bench-scale flotation experiments of a nickel-copper sulphide ore were conducted in a Denver flotation cell. The IGC analyses were carried out on the timed concentrates, as well as on the final tailings, in order to evaluate the correlation between surface energetics and the flotation response of the ore particles. The results indicated that the floatability of the concentrates was directly related to the surface energy of the particles. Both dispersive and specific components of surface free energy increased by increasing the necessary time for the particles to be floated, which was consistent with the obtained values for the work of adhesion to water. However, the flotation response of the tailings was not consistent with the expectations from the surface energy value. The significance of the sample composition, particle size distribution, and their consequences in surface energy-flotation response relationship were also observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".