Application of genomic innovations in mine operations, reclamation, and closure
Bibliographic record
Abstract
British Columbia (BC) is at a pivotal juncture in its efforts to expand the critical minerals sector sustainably. It is essential for the province to actively develop innovative and sustainable methods to minimize the impact of mining activities on water, air, land, and biodiversity. Increased pressure from investors is encouraging mining companies to improve their disclosure of environmental impacts and adopt alternate technologies to reduce their ecological footprint. In response to these challenges, genomics has emerged as a promising solution, capable of detecting cryptic biodiversity, providing early indicators of reclamation progress, recovering critical minerals from mine waste, and improving water quality. This paper provides an overview of the current genomics technologies and approaches, including environmental DNA and microbial genomics, that are applied to mine operations, reclamation, and closure efforts. It will include examples from collaborative research and partnerships among academia, industry, Indigenous communities, and government in BC and Canada, supported by Genome BC, Genome Canada, and beyond. Additionally, the paper will discuss the ongoing efforts to develop industry standards for genomics technologies, which are important for ensuring regulatory compliance and promoting the adoption of these solutions in the mining sector. As BC expands its critical mineral sector, it will be important to strike a balance between the economic advantages of mineral resource development and the preservation of the environment for current and future generations. By fostering innovation and collaboration among multidisciplinary teams and sectors, and with support from programs from Genome BC, the province can adopt sustainable mining practices that benefit the well-being of its natural ecosystems and communities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".