Bibliographic record
Abstract
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".