Ontario's mineral sector: : "Enriching the future"
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
Home to some of the world's leading mining technology specialists, Ontario has long been at the forefront of the modern mining industry. Michael Gravelle, Ontario's Minister of Northern Development and Mines, outlines how the province is working to reinforce its status as a global mining destination. The provincial government has a proud tradition of collaboration with industry partners and organisations, including the OPA, the Prospectors and Developers Association of Canada (PDAC) and the Ontario Mining Association. These relationships are crucial to strengthening the mineral development industry, fostering research and innovation, increasing investment and risk capital and ensuring Ontario continues to enhance its competitiveness. The province recently released a renewed Mineral Development Strategy - a 10-year vision to position Ontario as the global leader in sustainable mineral development and a blueprint for building on the region's global reputation as a premier mineral development destination. The Ontario government's support remains a key pillar in the future growth of the province's mineral sector, especially as the world's need for minerals expands
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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