Study on the preparation of high performance concrete using steel slag and iron ore tail-ings
Bibliographic record
Abstract
At present, mineral admixtures have become an essential component and functional material of the concrete technology.The use of such materials in concrete can significantly reduce the CO 2 emissions of the cement industry [1-3].As the mineral admixture, the granulated blast furnace slag (GBFS) and fly ash have been widely used in concrete [4], so that they have gradually become scarce resources in many cities.The iron and steel metallurgy industry is the economic foundation of the country.With its continuous development, the related problems such as resource development, energy consumption and pollutant emissions have become increasingly serious.Steel slag (SS) is one of the main solid wastes in the production process of the iron and steel metallurgy industry, and its emissions are about 15-20% of crude steel output [5].China's annual steel slag production is about 80 million tons, with a cumulative storage of about 500 million tons, while its comprehensive utilization rate is only 22%.Now there are several methods for comprehensive utilization of the SS at home and abroad, e.g., the SS is used as metallurgical raw materials (sintered materials, blast furnace flux, etc.), new building materials, and the ingredients of glass-ceramics in the road engineering, environment and agriculture, etc.However, it hasn't been widely used in cement concrete.Wang et al. [6] and Sun et al. [7] found in their study that the SS should be taken as a mineral admixture, which is the most important way to achieve the efficient use of SS resources in cement concrete.Therefore, steel slag is a potential active mineral admixture.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".