Expanding Foothills Exploration in Muskwa – Kechika
Bibliographic record
Abstract
In the summer of 2005, ARKeX Ltd. in conjunction with JEBCO Seismic Canada began acquiring an airborne geophysical survey over Muskwa-Kechika (MK) covering 3,000 sq km of the Rock Mountain Foothills, British Columbia. The survey was completed in 2006 and the data acquired provides the explorationist with previously unattainable high resolution airborne gravity gradiometry, magnetic gradiometry and LIDAR data. This combined dataset reveals new and detailed information of geologic structures over this expansive area. Through qualitative and quantitative interpretation of the dataset, shallow and deep structural targets are readily imaged, even beneath 1.5km of terrain. This project represents state-of-the-art deployment for Airborne Gravity Gradiometry (Air-GG). The way in which the data was acquired, processed and interpreted represents a significant deviation from existing methodology. In this paper, we demonstrate the advantages of the dataset by focusing on the exploration history of a well drilled in 1993. The target, a deep Mississippian carbonate play in the Debolt Formation, 4 km beneath the surface. Using qualitative and quantitative interpretation techniques, we demonstrate, with hindsight, that the addition of Air-GG to the ‘then’ exploration datapool would have significantly increased the probability of technical success. We therefore discuss how we can use this example as an analogue to help explore in other areas of MK while avoiding costly mistakes.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".