Timing effects on the fragmentation of small scale blocks of granodiorite
Bibliographic record
Abstract
A series of small scale tests, simulating multi-hole blasts have been performed to establish the effect of delays on blast fragmentation. The blasts were performed in high quality granodiorite blocks, which were cut from stone prepared by dimensional stone quarry operations. The pattern used was equilateral triangular, with a distance of 10.2 cm between boreholes, which had a diameter of 11 mm, were loaded with detonating cord and the coupling medium was water. The delays used were achieved using different lengths of detonating cord for the cases of delays between 0 and 100 μs between holes and a sequential blasting machine firing seismic detonators for larger delays up to 4 ms. All fragments were collected and screened. The experiments showed that the worst fragmentation was achieved with simultaneous initiation of all charges. Fragmentation improved with the delay time between holes up to 1 ms between holes. If the experiments are scaled up, the results show that in granodiorite, fragmentation optimization requires delays of few milliseconds per metre of burden. The findings, agree with previously published work, involving larger scale experiments and other rock types.
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 | 0.000 | 0.001 |
| 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".