Surficial geology, geomorphology, granular resource evaluation and geohazard assessment for the Maxhamish Lake map area (NTS 94O), northeastern British Columbia
Bibliographic record
Abstract
As part of the Geo-mapping for Energy and Minerals Program (GEM-Energy) Yukon Basins Project, the Geological Survey of Canada (GSC) and British Columbia Ministry of Energy and Minerals (BCMEM) collaborated between 2009 and 2011 to produce a digital surficial geology and landform (geoscience) map and an accompanying geodatabase of field observations, terrain units, landforms and geomorphic processes in the Maxhamish Lake map area (NTS 94O), British Columbia. In this paper, we present the distribution of surficial deposits and landforms; and describe the sedimentology, surface morphology and facies associations of major terrain units and landforms. This terrain inventory is evaluated to: a) better define the regional potential for granular aggregate and frac sand resources in the map area; b) identify key geohazards that could impact surface infrastructure (e.g., road design, well pad locations, pipeline routing); c) provide baseline information useful for future land management decisions on resource development in northeastern British Columbia. By providing government agencies, industry, communities and the public access to reliable geoscience information on surficial earth materials, geohazards and granular resource potential and groundwater, the risks for new investment, exploration and development of natural resources in northern British Columbia will be reduced.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".