MétaCan
Menu
Back to cohort

Perfection of a technology of processing and utilization of fine fractions of dump slag

2020· article· en· W3045367179 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFerrous Metallurgy Bulletin of Scientific Technical and Economic Information · 2020
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsSlag (welding)Magnetic separationMetallurgyFraction (chemistry)Materials scienceRefining (metallurgy)Extraction (chemistry)Inclusion (mineral)Waste managementMineralogyEngineeringGeologyChemistry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.211
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it