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
This work contains the results from experimental testing on the process of oxidation copper leaching under pressure from concentrate obtained after the re-flotation process of tailings. Flotation tailings are a significant resource for recovery of copper and other useful components, since their content in tailings is approximate to the content in the primary raw materials. On the other hand, the dumped flotation tailings, under the influence of the atmosphere, pollute the surrounding land, surface waterways, as well as the groundwater, and present a serious environmental problem. Larger global companies are involved in research related to the process of obtaining the useful components from tailings. Testing the flotation process at the site Katanga [1] was focused on valorization of copper and cobalt from tailings. In the Musselwhite Mine in Ontario, the process of flotation concentration [2,3] has confirmed that this is an effective method for reducing the content of sulphides present in tailings. The effect of sulfuric acid concentration as a leaching reagent was tested, as well as the effect of temperature, pressure, and pulp density on the degree of copper leaching. The obtained results indicate that the use of combined procedure of re-flotation tailings and copper leaching under pressure achieve a high degree of copper separation of over 98%.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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