Mine Backfill, An Operator’s Guide. An interactive CD-ROM for mine operators and engineers.
Bibliographic record
Abstract
“Mine Backfill, An Operator’s Guide ” is an extension of the electronic book on CD-ROM entitled “Mine Backfill 1998”, which was prepared for the CIM’s (Canadian Institute of Mining, Metallurgy, and Petroleum) centennial celebrations in 1999. Discussions with many operations revealed that there was an industry need for a backfill guide for front-line operator training and as a reference manual for operators and mine engineers. Sponsored by the mining industry and the CIM, “Mine Backfill, An Operator’s Guide ” has been developed by the Departments of Mining Engineering at Queen’s University and McGill University to fulfil this need. “Mine Backfill, An Operator’s Guide ” is an interactive multimedia package that incorporates high quality colour images, animations, sound, and video with text to review the main backfill methods employed in Canada, including hydraulic/slurry, paste and rock fill. Based on a combination of engineering theory, visits to mines, and discussions with operators, the Guide discusses and reviews key issues, practices, and technology currently in use. The Guide has been designed to be user friendly, includes extensive help features, with operators in mind, and is being distributed on CD-ROM. The Guide is divided into training and reference sections. When in training mode, the operator is guided through a presentation of common mining and backfill practices and technologies so that he/she may become more familiar
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.488 | 0.368 |
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".