Borehole magnetics navigation: An example from the Stratmat Deposit, Bathurst, New Brunswick
Bibliographic record
Abstract
Information acquired from studies of borehole core or from borehole geophysical logs form an integral part of all mineral and oil exploration programs. Yet, as is the case of any survey, the value of that information is dependent upon how well the location of each observation point is known. Location information becomes especially critical when the resource target has limited depth extent. For example, a location error of 10 m when evaluating a 5-m thick gold vein can make the difference between an economic and noneconomic deposit. The trajectory of a borehole is commonly computed using an array of data points that are acquired progressively with increasing distance along the borehole. Depending on the type of survey employed, observation points may be sparse (static readings at a limited number of points) or redundant (dynamic surveys where the distance between observation points is less than the length of the rigid probe). At each observation point, three parameters are normally acquired: (a) inclination, dip of the borehole relative to horizontal; (b) dip direction, the orientation of the maximum dip direction relative to geographic north; and (c) depth, usually measured as distance along the borehole.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".