Preparation of environmentally friendly and energy-saving autoclaved aerated concrete using gold tailings
Bibliographic record
Abstract
Gold tailings (GTS) are solid wastes from gold mining operations by mining enterprises.At present, China's GTS are basically in a state of tailings storage.According to statistics, from 2014 to 2018, the national GTS emissions reached 920 million tons, which brought serious environmental and safety problems to the mining area.Therefore, it has become a top priority to conduct secondary development and utilization of resources for the GTS [1].Due to its high silicon content, the GTS can be used as the admixtures to prepare autoclaved aerated concrete (AAC).This can effectively absorb a large amount of GTS, which makes it one of the key projects of the 12th Five-Year Plan for comprehensive utilization of bulk industrial solid wastes [2].AAC is a porous building material, and one of its most outstanding advantages is light weight, while the porosity is the cause of its light weigh [3,4].Some literatures show that the reason why the AAC's strength can be ensured under light weight is that during the autoclaving process, the hardened aerated concrete produced a large amount of well-crystalline tobermorite and crystalline phase calcium silicate hydrate (CSH), which cross-grow with gel-like substances to compact the structure and thus improve product strength and performance [5][6][7][8][9][10][11][12].After crushing, grinding and beneficiation, the fine-grained GTS are rich in silicate minerals.They differ greatly in the physical and chemical characteristics from acid materials such as fly ash and river sand that are commonly used in the production of aerated concrete [13,14].The active ingredients of silicate minerals, such as Al 2 O 3 and
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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".