A framework for managing contaminants at legacy mine sites
Bibliographic record
Abstract
Contaminant management at legacy and abandoned mines can be a complex and lengthy process, and often involves the management of more than one type of contaminant and contaminant source.This can be compounded by naturally occurring contaminants (e.g.metals, nutrients) in certain sedimentary rock formations that may be associated with coal, phosphate, uranium, or metals resources.Exposure of this rock and its placement as waste rock during mining can enhance the release of naturally derived "contaminants" through operations, closure, and reclamation.Upon entering receiving environment areas, the interaction between certain contaminants and receiving waters can enhance transformation into more biologically available forms that may be taken up more readily into the aquatic food chain.For mines that have already stopped operating, addressing risks associated with the ongoing release of bioavailable contaminants can be challenging.Legacy and abandoned mines can benefit from a site-specific management plan that characterises how contaminants move on the site and in the environment, and outlines and evaluates best achievable alternatives to mitigate the potential for effects.We developed an adaptive framework that outlines the relationship among effects linkages from sources, transport pathways, and aquatic receptors, and site-specific factors influencing contaminant fate and transport such as geochemical processes and water management.The framework is a tool that can be used to develop a pollution prevention hierarchy that prioritises source control followed by water management and treatment approaches, and potential mitigation activities with short-and long-term application, including site characterisation and monitoring needs.The use of this framework allows for a methodical and streamlined review of available site information, identification and filling of data gaps, and identification and evaluation of mitigation options that will complement closure activities.This paper will describe the development of the plan framework and its' application to address a variety of challenging contaminant management issues.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".