Canada-Nunavut Geoscience Office
Bibliographic record
Abstract
Abstract — The demographics of Nunavut indicate a very young and poorly educated population spread over wide areas in small isolated communities. The current economic sectors will be unable to expand and meet the demands of this emerging workforce. Given the realities of Nunavut, the most promising direction in which the economy may expand is through mineral development. The Nunavut Land Claims Agreement (NLCA) has established the legislative framework for sustainable development in Nunavut through mineral development. The NLCA has transferred ownership for much of Nunavut’s potential mineral resources to beneficiaries of the lands claim. Royalties gener-ated from mining operations on subsurface Inuit-owned lands, and to a lesser extent on Crown land, will flow back to beneficiaries of the lands claim through their administrative company, which may then be invested in the communities. Additional benefits of mineral resource development in Nunavut will be realized through impact and benefit agreements. To realize the benefits from mining operations, Nunavut has to be able to attract and support exploration and development companies through the release of high-quality geoscience data. How-ever, due to Nunavut’s isolation and the expense of conducting geoscience fieldwork, the jurisdiction lags far behind the rest of Canada in terms of quality and quantity of government geoscience. About
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.124 | 0.020 |
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".