Designing a Large-Scale Lake Cooling System for an Ultra-Deep Mine: A Canadian Case Study
Bibliographic record
Abstract
Subsurface mining operations are continuously getting deeper and more complex due to depletion of shallow deposits. This fact inevitably brings more expensive, high-tech oriented and most importantly energy intensive subsurface mining operations to come alive. Accordingly, while big mining companies are developing sensible extraction methods to exploit orebodies located at great depths, they are also seeking to cut down their costs and carbon footprint. A large percentage of the energy needed by a subsurface mine is due to the mine ventilation and air conditioning reasons. In fact, for mines deeper than 2 km, mine air conditioning becomes a must. Yet, as there are not many alternatives developed, most of the modern mines are subjected to deploy tens of megawatts worth of cooling plants using massive refrigeration units. This does not only create a large financial burden during the project stage but also results in heavy energy demands during the operation. This paper aims to investigate a natural, alternative deep-mine lake cooling system by providing a detailed ‘front-end-loading’ design conducted for a real-life, Canadian example.
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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".