Supplemental data from: Manganese ore systems: A Canadian perspective on a critical element in the transition to a sustainable green economy within North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 desulphurising 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 battery component for electric vehicles. 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, their mineral composition makes them more suitable for electric vehicle batteries than most current mines or in the future manganese nodules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 it