Genomics to assist mine reclamation: a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it