Bibliographic record
Abstract
Deep and high stress mining poses a significant number of geotechnical challenges. Despite considerable improvements in almost every technological aspect of the design and operation of mines at depth and under high stress, the weakest link remains the quality and quantity of data. This paper addresses certain inconvenient facts on how data are collected and managed. The case is made for a disciplined approach to data collection, analysis and interpretation. Unless this is implemented in a systematic way, it will not be possible to capitalise in gains made from improved engineering tools. A further concern is that for mines operating at depth, the margin of error due to inadequate or inappropriate data is much smaller, and the repercussions more severe, compared to shallow mines.
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 | 0.054 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.028 | 0.036 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.040 |
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".