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Approaches for the Remediation of Abandoned Mines and NOAMI

2009· article· en· W3173707614 on OpenAlexaboutno aff
Gilles Tremblay

Bibliographic record

VenueEnvironnement Ingénierie & Développement · 2009
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsRemedial educationEnvironmental planningPolitical scienceEnvironmental remediationPublic policyPublic administrationPublic relationsEngineeringGeographyLaw

Abstract

fetched live from OpenAlex

The National Orphaned/Abandoned Mines Initiative (NOAMI) was established in 2002. The multistakeholder nature of NOAMI has provided a uniquely Canadian opportunity for governments, non-governmental organisations, Aboriginal Canadians and the mining industry to discuss issues and barriers associated with the clean-up and remediation of orphaned and abandoned mine sites. This convergence of interests and mutual commitment to progress has fostered the success of this internationally recognized approach to influencing public policy and addressing issues of common concern.Over the past 5 years, NOAMI has been working diligently to influence policy and build capacity in Canada to address these issues. Various workshops, conferences and publications have provided the background information, analysis and network building that have driven the agenda forward. During this time, there has also been a substantial increase in remedial activities carried out by the jurisdictions across Canada. This paper provides a five-year summary of NOAMI’s efforts and an overview of the remedial activities in the Canadian jurisdictions. The jurisdictional highlights feature many of the different approaches and partnerships employed across Canada.The paper also includes several international case studies of novel regeneration projects completed on legacy sites.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.026
GPT teacher head0.210
Teacher spread0.184 · 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 designOther design
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

Citations2
Published2009
Admission routes1
Has abstractyes

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