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Record W4388796175 · doi:10.3390/su152216086

Water Impacts and Effluent Quality Regulations of Canadian Mining

2023· article· en· W4388796175 on OpenAlexaboutno aff
Zauresh Atakhanova

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersNazarbayev University
KeywordsTailingsEffluentWater qualityCoal miningEnvironmental scienceMineral resource classificationWater resourcesNatural resource economicsBusinessCoalEnvironmental engineeringWaste managementEngineering

Abstract

fetched live from OpenAlex

Energy transition relies on the scaling up of mineral production and may lead to increased pressure on water resources due to the intensity of water use in mining. The status of Canada as a major mineral producer and a country with effective environmental regulations prompted our study of the water impacts of Canadian mining. In 2002, the Canadian government introduced effluent quality regulations that targeted metal mining companies. By analyzing regional and sectoral data, we find that such regulations were important for mitigating both the water quality and water quantity impacts of metal mining. Despite increasing output, metal mining reduced its contribution to total mining withdrawals and discharge from 85% in the pre-regulation period to 62–65% in the post-regulation period. In the absence of such regulations, non-metallic mineral mining and, in particular, coal mining, increased their pressure on water resources. Finally, we find that since 2002, over 90% of regulated operations have met effluent quality standards. However, we document increased flows of discharge to mine tailings, a development which requires further analysis.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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 designObservational
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

Citations7
Published2023
Admission routes1
Has abstractyes

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