In situ sensor-based monitoring strategies for biogeochemical reactions in mine tailings environments
Bibliographic record
Abstract
Natural resource extraction and ore processing have significant environmental impacts, such as the generation of wastewater, waste rock and tailings. These waste products are often detrimental to ecosystems, and negatively impact surface and groundwater bodies, often necessitating remediation treatments and long-term management of sites by operators, or, where operators have abandoned a contaminated site, by regulators and government agencies. Such cleanup and monitoring efforts regularly continue for decades after a site is closed. Monitoring efforts usually serve two purposes: characterizing the long-term changes at a site once extraction and processing activities have ceased and evaluating the effectiveness of applied remediation treatments. Monitoring activities are usually mandated in the site’s operating license and usually include frequent field sampling of surface water, groundwater, and soil or sediment, as well as ecological studies describing floral and faunal abundances. These samples are then analyzed to quantify the mobility and phase of contaminants (i.e., toxic heavy metals, hydrocarbons), fundamental water quality parameters (i.e., pH, TDS, alkalinity), and the makeup and function of the microbial community (i.e., culturing, microcosms, ‘omics). The need for skilled workers and constant on-site personnel presence means that environmental monitoring is a high- cost activity for site operators and is a significant financial burden for government and regulatory agencies tasked with managing abandoned legacy mine sites. Over the last decade, rapid developments in platforms for deploying remote scientific instrumentation, lower-cost environmental sensors, and data transmission from remote locations have brought about a renewal of interest in sensor-based environmental monitoring strategies. These approaches offer several advantages, such as lower cost, near real-time data access, and lower exposure risk to toxic and hazardous materials. Here, we will present data collected from a suite of electrochemical sensors deployed in situ at a closed, managed mine site to monitor the effectiveness of remediation treatments in real-time. These results provide proof-of-concept for the effectiveness of sensor-based monitoring technology as part of safe, effective long-term remediation and management strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".