A novel method for rock fracturing with soundless chemical demolition agents in subzero ambient temperature
Bibliographic record
Abstract
Interest in explosive-free rock fracturing has grown exponentially in the past two decades due to concerns about the environmental impacts of traditional rock fracturing methods with explosive energy. The present paper essentially presents the findings of an experimental study that aimed to develop a novel method for rock fracturing with soundless chemical degradation agents (SCDAs) at cold ambient temperatures. This is of great significance because commercially available SCDAs do not perform well, or at all, in these conditions. This study is part of a multi-phase project under the umbrella of Canada's Clean Growth Program. The newly established approach is verified through the rock fracture test results with SCDAs of two series of concrete and granite blocks exposed to cold temperatures up to −20°C. This method, the so-called high-temperature wire method (HTWM), utilizes a high-temperature spiral wire inserted into the SCDAs borehole and subjected to a DC voltage. The heat flux generated by the wire helps the SCDAs cure and expand to fracture the block. Based on the experimentally obtained results, it has been shown that the proposed new HTWM is quite promising because it enables rock fracturing with SCDAs at cold ambient temperatures reaching −20°C. It should be emphasized that such a result was previously unattainable with current industry practice.
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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) | 0.000 | 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) | 0.001 | 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".