Wanted: A Legal Regime to Clean Up Orphaned/Abandoned Mines in Canada
Bibliographic record
Abstract
This article describes the environmental, social, and economic problems posed by orphaned and abandoned mines and summarizes the state of Canadian law on the issue. Orphaned and abandoned mines are those for which the owner cannot be found, or for which the owner is financially unable to carry out cleanup. There are an estimated 10,000 such mines in Canada and more than 5,700 in Ontario alone, with cleanup costs expected to be in the billions, paid predominantly by taxpayers. Current laws operate on the assumption that a responsible person is available, upon whom regulators may impose obligations. Under these laws, an orphaned or abandoned mine, which by definition has no responsible person, is implicitly presumed not to occur. These laws largely do not apply to orphan/abandoned mines, and have not developed mechanisms for addressing them, other than through an emergency response by government using public monies to remedy the problem. Financial security requirements have also proven to be a weak link in existing legislation. Predictions of the quantum of financial security needed from applicants to ensure proper closure and rehabilitation been inaccurate. In these cases, when mining companies became insolvent or disappeared, funding necessary to avoid major shortfalls in cleanup costs had to be provided by the government, with little expectation of cost recovery. A solution to this situation will require legislative reform, including imposing fees on mining companies that will allow governments to establish dedicated orphaned and abandoned mine funds to finance cleanups.
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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 | 0.001 | 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 | 0.001 |
| 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".