Assessing longwall support-roof interaction from shield leg pressure data
Bibliographic record
Abstract
A concept has been developed where characteristic load cycles of longwall shields can describe most of the interaction between a longwall support and the roof. A characteristic load cycle is the change in support pressure with time from setting the support against the roof to the next release and movement of the support. The concept has been validated through the back-analysis of more than 500 000 individual load cycles in five longwall panels at four mines and seven geotechnical domains. The validation process depended upon the development of new software capable of both handling the large quantity of data emanating from a modern longwall and accurately delineating load cycles. Existing software was found not to be capable of delineating load cycles to a sufficient accuracy. Load-cycle analysis can now be used quantitatively to assess the adequacy of support capacity and the appropriateness of set pressure for the conditions under which a longwall is being operated. When linked to a description of geotechnical conditions, this has allowed the development of a database for support selection for greenfield sites. For existing sites, the load-cycle characteristic concept allows for a diagnosis of strata-support problem areas, enabling changes to be made to set pressure and mining strategies to manage better, or avoid, strata control problems. With further development of the software, there is the prospect of developing a system that is able to respond to changes in strata-support interaction in real time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".