Bibliographic record
Abstract
The sintering performance of Canada iron concentrate and metallurgical performance of sinter product was studied, the physic-chemical properties was investigated. The results show that the sintering indexes and metallurgical performance of sinter product both get some worse with the mixing rate of concentrate increasing from 24% to 42%. The permeability resistance of sinter bed increases from 857 Pa to 1 150 Pa,the vertical sintering speed drops to 18.38 mm/min from21.87 mm/min, the productivity declines from 1.14 t/(m2·h)to 0.93 t/(m2·h),the tumble index falls to 64.13% from67.47%, the anthracite solid fuel consumptions increases to 70.42 kg/tfrom 62.46 kg/t, meanwhile, the reduction index(RI)and the reduction degradation index RDI3.15 mmfor sinter product drops from 82.31% and 70.71% to 78.76% and 64.41%,respectively. This kind of concentrate was bad for granulating of mixture because of its major mineral-specularite and martite whose mineral surface has poor hydrophilicity-and unreasonable size composition, meanwhile, it is hard to generate enough liquid during sintering due to its dense and smooth surface and poor sintering capability-its softening temperature is more than 1 450 ℃. Therefore, poor granulating performance of Canadian concentrate leads to worse layer permeability then cause poor sintering performance, while high softening temperature results in the tumble index decreases when the mixing rate of concentrate increases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".