Spray freezing for mine heating a statistical perspective
Bibliographic record
Abstract
To meet Ottawa’s net-zero emissions target by 2050, it is imperative for the mining industry to move towards sustainable energy sources. Spray freezing has been demonstrated to be an effective substitute for the fossil fuels to supplement the heating needs of the mines operating in the sub-arctic climates. This study presents a reduced-order theoretical-statistical model that captures the freezing process of a droplet accurately. A novel droplet freezing model, using the principles of statistical thermodynamics, is developed that takes into account the supercooling, crystallization, equilibrium freezing and solid subcooling stages. The model also considers evaporation, sublimation and radiation. The theoretical model for a single droplet is further extended to a spray configuration to compute the thermodynamics of the process on a system scale. Results indicate that the model robustly predicts the thermal characteristics of the spray freezing potential for mine heating. The heat rate as predicted by the model compares to within 5 % of the field data.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".