GRAVILOG: An update on the development and use of Borehole Gravity for Mining Exploration
Bibliographic record
Abstract
Scintrex GRAVILOG borehole gravity meters are now successfully deployed by Abitibi Geophysics crews in boreholes for mining exploration in Canada and USA, and by Scintrex and Micro-g LaCoste crews in wells used for CO2 injection, sequestration and leaching in Canada, USA and Europe. In 2013, GRAVILOG systems and crews will be available in Europe, Russia, Brazil and Australia. Scintrex has developed and will be testing a dual sensor GRAVILOG probe in early 2013.A brief review of the GRAVILOG development and specifications is presented, followed by several recent case histories. The result from Donner Metals / Xstrata Zinc’s Bracemac KT Zone in the Matagami region of Quebec is the first documented use in mining exploration of borehole gravity to measure excess mass coincident with a borehole EM conductor. Forward modeling and inversion of GRAVILOG data from multiple holes has proven effective in outlining the mineralization and estimating the tonnage of the Virginia Mines’ Lens 44 on the Coulon property in Quebec. Apparent bulk density measurements from multiple holes at the Labrador Iron Mines’ James South Extension iron ore deposit near Schefferville, Quebec reduce drilling costs and time needed to obtain this information.A major source of error in bulk density measurements of thin beds is minimized by the dual sensor GRAVILOG system. In addition to eliminating the error in the depth interval between the sensors, common mode noise rejection improves the gravity difference data.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".