Manganese ore systems: a Canadian perspective on a critical element in the transition to a sustainable green economy within North America
Bibliographic record
Abstract
Manganese is a critical metal for modern economic development. Over 85% of manganese is used in the production of steel, where, as a desulphurizing agent, it is an essential additive in all steelmaking, and as an alloying agent it makes a range of specialty steels. Additionally, it has significant uses in fertilizer, animal feed, rubber, glass, unleaded gasoline, ceramics, and paints. Currently over 80% of manganese ore comes from a few mines in Africa and Australia, but its production is facing two potential paradigm shifting developments over the next decade: the potential extraction of manganese nodules from the ocean floor; and within the transition to a green economy as a component for electric vehicle batteries. Herein we present the first national inventory of Canada’s manganese occurrences in 90 years. While Canada has no current production, it does contain many occurrences, though most are too small to be viable operations. Presently, the deposits most suitable for mining are in the Woodstock area, New Brunswick. Not only are these relatively large tonnage and high grade, but their mineral composition also makes them more suitable for electric vehicle batteries than most currently mined manganese ore or, potentially in the future, from seafloor mining of manganese nodules.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".