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Record W2019371723 · doi:10.1177/0734242x15574562

Controlling chromium slag pollution utilising scavengers: A case of Shandong Province, China

2015· article· en· W2019371723 on OpenAlexaff
Changhao Liu, Raymond P. Côté

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2015
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsDalhousie University
Fundersnot available
KeywordsChromiumSlag (welding)ChinaPollutionEnvironmental pollutionEnvironmental scienceWaste managementMetallurgyEnvironmental protectionBusinessMaterials scienceEngineeringGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.306
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2015
Admission routes1
Has abstractyes

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