Preliminary Discussion on Comprehensive Utilization of Tailings
Bibliographic record
Abstract
Tailings produced in a concentration plant are the discharge of solid wastes after grinding ore into size and selecting useful components in the specific economic and technological condition. According to statistics, for the mining of metal ore, non-metallic ore, coal, clay, etc, the production of tailings of the world is up to 100 million tons per year. The number of existing tailing piles is 12718 in China, of which the construction ones are 1526, accounting for 12% of the total, and the closed tailing piles are 1024, accounting for 8%. As of 2007, the national total tailings accumulation is 8.046 billion tons. The non-ferrous metal mining is one of the largest discharge of solid wastes industries because of its low comprehensive recovery rate. For example, the beneficiation and recovery rate of non-ferrous metal mine is from 50% to 60% in China, which is lower 10% to 15% than developed countries, and the associated non-ferrous metal recovery rate is 40%, which is lower 20% than developed countries. On the utilization of duns, Poland is 90% to 100%, United States, Australia, France, Canada, Belgium and other countries followed, while China is only about 20%. The utilization of fly ash is to 100% in Japan and Denmark, France is 65%, UK is 55%, and China is just 45%. Quantities of waste rock, waste slag, and waste water have occupied land, destructed vegetation, deteriorated the soil and water quality, and caused land subsidence, landslides, mud-flow and other geological disasters. Therefore, the comprehensive utilization of resources is the right choice of mining sustainable development, environment protection, resource conservation, economic and social development.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".