Perfection of a technology of processing and utilization of fine fractions of dump slag
Bibliographic record
Abstract
Processing of old dumps slag at crushing-sorting facilities results in a large yield of slag fine fractions – screening. Because of high content of metal inclusions and powder-like fraction in the screening, this product often becomes unclaimed and is returned to dump. Magnetic separation can increase the consumer properties of the slag screening, but it is only magnetic product that is returned to the processing while the mineral part is left in the dump. Basic characteristics of 0–10 mm fraction quoted. It was determined by laboratory study, that after extraction of ferromagnetic inclusions, the true screening density decreased by 17.5% and bulk density – by 3.1%. The powder-like inclusions remain in the screening, that does not allow to consider the material as a filler for concrete and macadam-sand mixture for road-building. To recycle the magnetic product, extracted out of the screening, decreasing of slagging is needed, as well as systematic evaluation of sulphury inclusion content in it. It was proposed to divide the slag screening for macadam and sand fractions after maximum possible removal magnetic inclusions and powder-like inclusions. It will allow to return into the utilization a part of iron previously lost with the fraction 0–10 mm and to obtain an iron concentrate for sintering. The remained tails in the form of macadam of 5–10 mm fraction, sand 0–5 mm and meliorant with decreased iron content can be used in construction industry and agriculture. By field tests it was determined, that adding meliorants, which contain a grinded screening with oxides of magnesium, phosphor and microelements, into soil, results in increasing crop capacity of vegetables by 30% at test areas.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".