Genomics to assist mine reclamation: a review
Bibliographic record
Abstract
Mine reclamation succeeds when healthy, self‐sustaining ecosystems develop on previously mined lands. Regulations require reclamation of ecosystem services; however, there are few specified targets, and those that are presented are vague. Sequencing genomic DNA and transcribed RNA from environmental samples may provide critical supportive information for attempts to recreate ecosystem functions from the ground up on disturbed lands. In this review, we highlight the use of genomics to meet mine closure goals, to enhance ecosystem development, and to optimize ecosystem services inherent in self‐sustaining reclaimed ecosystems. We address the development of environmental genomics—sequencing and analysis of environmentally derived DNA —to characterize microbial communities on mine sites. We then provide four areas where genomics has proven instrumental for informing management and assisting in reclamation of mine sites in the form of bioreactors, passive treatment systems, novel gene discovery, and DNA barcoding. Finally, we describe how recently developed techniques have transferable value to mine reclamation and provide evidence for future applications of genomics and the necessary steps to integrate these data into comprehensive management of mined sites.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".