Underground collision avoidance: From technology to solution
Bibliographic record
Abstract
Approximately one quarter of fatalities in both the Australian and US mining industries can be attributed to vehicle accidents. While the total number of fatalities has shown a downward trend over the past decade, the number arising from vehicle accidents has remained moreor- less constant. These trends suggest that safety initiatives in mining have been successful in reducing overall deaths in the industry but that the dangers associated with human-vehicle interaction have not been adequately addressed. In the last five years there has been a proliferation of collision avoidance systems designed for surface mining applications. Many of these systems have been introduced with varying degrees of success across diverse operations but in general, wide acceptance by the industry has not been achieved. The development and implementation of systems specifically tailored for underground mining operations, where vehicle related fatalities are equally common, has occurred at a far more subdued pace. There are a great many challenges, unique to the underground application, confronting both the underlying technology as well as its translation into a useful solution. This paper examines the current state of collision avoidance systems applied to the underground mining context and attempts to outline some sensible steps that may assist reducing the number of fatalities. © CRCMining 2010
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".