Bibliographic record
Abstract
Over the last three decades there has been significant technological change in both coal mine monitoring and ventilation modelling capabilities in the underground coal industry. During this time there have been substantial changes in statutory ventilation monitoring requirements to reflect such changes in our industry’s monitoring capability. Somewhat contrastingly, there has been very little change in the mandatory regulatory requirements relating to ventilation quantity and gas contamination readings that need to be taken on a monthly basis. On top of that there has been no official mention made of any requirements to model or predict ventilation circuit changes in the mine office, before attempting such procedures underground. Using the NSW coal mines regulations as the basis for the opinion it is the writer’s belief that whilst the skill level requirements of minesite ventilation officers has been substantially elevated over the last quarter of a century there has been no step change in the mandatory output requirement of the monthly statutory ventilation survey and accompanying report. In the 20th century it was not uncommon for the mine ventilation survey to comprise a handful of readings which were then entered into a pro-forma book and then hidden away until the next survey. The data measured was insufficient to accurately delineate the circuit and little or no thought was given to utilising the data to build a mimicked model of the ventilation circuit. Unfortunately in the 21st century there has been insufficient regulatory change to date to ensure such basic practices are elevated to a higher level. Current ventilation modelling software programs are very user friendly and relatively simple to maintain and update but even the most recent changes in the regulations have all but ignored the value of such tools. With some careful planning the ventilation surveyor should set up both his quantity and pressure survey stations in a manner which will afford not only statutory compliance but allow him to accurately define the ventilation circuit to a level that will allow him to maintain an accurate ventilation model of the circuit. The maintenance of such a tool will afford far safer change procedures relating to ongoing circuit adjustments. The final ventilation report that is assembled should be a useful tool and ideally communicated to the frontline supervisors to assist them with their ongoing understanding of the circuit in which they work. It is hoped that some of the tips contained in this paper relating to report inclusions and representation of such data may help at least some ventilation officers streamline their monthly data collection/collation process and with a minimum amount of work. The output is an accurate snapshot of the circuit to utilise for model validation that should double as a useful training tool. The Australian coal industry has an outstanding safety reputation and we must ensure we properly utilise all available technologies to keep it that way.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".