Canada's got your tung? A wealth of opportunity
Bibliographic record
Abstract
Tungsten is an essential metal used in high-heat, high-strength, and high-density applications, making it critical to technologies that support a green and sustainable future. However, global supply is dominated by China, raising concerns about environmental, social, and supply chain risks. Canada, once a significant producer, holds about 20% of the world's tungsten reserves and has the potential to re-emerge as a leading supplier amid rising demand. Tungsten is concentrated in Earth's ancient continental crust and mobilized by tectonic, magmatic, and hydrothermal processes during mountain building. In Canada, most mineralization is linked to two major orogenic events: the Paleozoic Appalachian Orogen in the east and Mesozoic to Cenozoic mountain-building in the Canadian Cordillera. These events produced two principal deposit types, including some of the country's largest resources: granite-hosted veins (e.g., Northern Dancer and Sisson Brook) and skarns (e.g., Cantung and Mactung), among the world's richest. Exploration combines regional methods (e.g., geophysics and mapping) with targeted techniques like till geochemistry and indicator mineral analysis. Despite Canada's strong geological endowment, the remoteness of many deposits remains a barrier. Strategic investment in infrastructure and exploration is essential to unlocking Canada's potential as a sustainable and secure tungsten source.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.007 |
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".