Controlling chromium slag pollution utilising scavengers: A case of Shandong Province, China
Bibliographic record
Abstract
The problem of chromium slag pollution is a great challenge for China. It is now an urgent task for China to take effective measures to eliminate chromium slag pollution. This article examines the case of the treatment of chromium slag in Shandong Province and explores how chromium slag pollution can be eliminated in Shandong Province. It shows that the chromium slag stockpiled by the chemical plants was successfully utilised by local steel companies, who act as 'scavenger companies'. The driving mechanism, seeking a potential 'scavenger company' within the local region and the role of the local government on the case of Shandong Province are discussed. This article concludes that local steel companies can be utilised to effectively and efficiently treat the chromium slag while benefiting the steel companies. The local governments need to play multiple roles in solving the problem of chromium slag pollution. Seeking and identifying 'scavenger companies' within a region could be an important approach to reducing pollution within the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".