Overview of GEM surficial geochemistry and indicator mineral surveys and case studies in northern Canada
Bibliographic record
Abstract
As part of the Geo-mapping for Energy and Minerals (GEM) program between 2008 and 2020, the Geological Survey of Canada carried out reconnaissance-scale to deposit-scale geochemical and indicator mineral surveys and case studies across northern Canada. In these studies, geochemical methods were used to determine the concentrations of 65 elements in lake sediment, stream sediment, stream water, lake water and till samples across approximately 1,000,000 km2 of northern Canada. State-of the-art indicator methods were used to examine the indicator mineral signatures in regional-scale stream sediment and till surveys. This research identified areas with anomalous concentrations of elements and/or indicator minerals that are indicative of bedrock mineralization, developed new mineral exploration models and protocols, trained a new generation of geoscientists and transferred knowledge to northern communities. The most immediate impact of the GEM surveys has been the stimulation of mineral exploration in Canada's north, focussing exploration efforts into high mineral potential areas identified in GEM regional-scale surveys. Regional- and deposit-scale studies demonstrated how transport data (till geochemistry, indicator minerals) and ice flow indicator data can be used together to identify and understand complex ice flow and glacial transport. Detailed studies at the Izok Lake, Pine Point, Strange Lake, Amaruq deposits and across the Great Bear Magmatic Zone demonstrate new suites of indicator minerals that can now be used in future reconnaissance- and regional-scale stream sediment and till surveys across Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.023 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".