Räjäytyksen aiheuttaman mikrorakoilun vaikutus kiven murskaukseen ja jauhatukseen
Bibliographic record
Abstract
This study is a part of a larger project being currently conducted at Queen's University in Kingston, Canada, called "Investigation of the overall efficiency of mining/milling operations through the optimization of energy utilization" funded by Natural Sciences and Engineering Research Council of Canada (NSERC). The project focuses on the optimization of mineral extraction process as a whole in opposed to the traditional approach, where the total process is classified into two groups as mining and milling and managed as separate cost centers. The Mine to Mill-project studies interrelated areas in mining and processing and tries to optimize the whole chain according to the findings. A series of laboratory experiments were conducted on two rock types, granodiorite and limestone, to study the effect of Powder Factor (kg/m3) on· damage and changes in the comminution properties of the rock. Blocks of rock were blasted with detonation cord (PETN) using different powder factors and the development of micro fractures was inspected from images taken from different samples under a polarized microscope. Test batches were created according to the powder factor used and to the assumed distance from the bore hole in order to obtain samples with different micro fracture density. Since granodiorite represents a fairly coarse grained and limestone a fine grained rock type, the effect of grain size on grinding resistance using different grinding methods, could be examined. The grinding methods included a Standard Bond Ball Mill and Rod Mill Tests, where the grinding resistance is obtained as Bond's Work Index, an approximate grinding test that gives a relative work index value and a SPI-test using a SAG-Mill. The results show that there is a reduction in grinding resistance as the powder factor is increased but other factors; the grain size in particular has a great effect on the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".