Bibliographic record
Abstract
The Geo-mapping for Energy and Minerals (GEM) program was funded between 2008 and 2020 with the aim of advancing geological knowledge of the North to reduce risk for mineral exploration and inform land-use decisions and future management of the North. Twenty-one regional activities were undertaken across Canada's northern shield, spanning northern Prairie Provinces, northern Quebec, Labrador, along with much of Nunavut and Northwest Territories. A further five activities were thematic in nature. Bulletin 612 presents results from 12 of these endeavours, including integrated regional bedrock geoscience studies, geophysical surveys, and basin analyses, as well as thematic thermochronology, geochemistry and large igneous province syntheses. The results highlight that GEM has contributed to new era of understanding of the northern Canadian Shield, expanding its framework substantially and developing an increasingly complex model of Archean cratons, Archean/Proterozoic microcontinents, and juvenile Paleoproterozoic crust that highlights the existence of a dozen new pericratonic to exotic ribbon microcontinents within a mosaic once considered as mostly large cratonic masses welded by Paleoproterozoic orogens. This emerging picture brings additional questions for future northern studies - particularly in the granularity of subdivision of the largest blocks, the impact of enigmatic earliest Paleoproterozoic orogens, and dynamics of assembly of exotic and little-known terranes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.319 | 0.136 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".