Flyrock Throw Calculations Unscientific and Unreliable – The “Hits” Just Keep on Coming
Bibliographic record
Abstract
Flyrock is the dirty little secret of the aggregate industry and its explosives engineers, and they have been remarkably successful in concealing the potentially deadly consequences of flyrock from the public, while continuing to engage in reckless blasting practices based on theoretical guesswork rather than proven practical land use planning safeguards such as permanent (fixed) onsite setbacks (excavation limits) coupled with permanent offsite separation distances from existing and potential future sensitive land uses. Flyrock is an unavoidable by-product of blasting rock, and has the potential to damage personal or real property, injure, permanently disable or kill humans and non-humans, both onsite and offsite. Flyrock, along with other adverse effects such as vibrations, that leaves the boundaries of an aggregate operation, constitutes nuisance and trespass, and damage or injury caused by detonation of explosives, should be held to strict liability. Implementing proactive and forward-looking land use policies that safeguard existing and envisioned sensitive land uses from the potentially deadly consequences of detonation of explosives in aggregate extraction is the most effective way to protect the long-term health, safety and welfare of existing and future generations, and to avoid interfering with the use and enjoyment of third-party real property.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".