Introduction et sommaire
Bibliographic record
Abstract
The Geo-mapping for Energy and Minerals (GEM) program (2008-2020) of the Geological Survey of Canada made important contributions to the understanding of northern sedimentary basins in the Canadian Arctic Islands, Hudson Bay, and the mainland Northwest Territories. The goal of the program was to advance the geological understanding of the Canadian north, which the exploration industry and northern communities could then use for decision-making related to exploration and development. The most important advances are outlined in the papers in this volume and summarized in this introductory paper. In each area, improvements in the stratigraphic understanding gained through fieldwork help researchers decipher paleoenvironments, thickness variations, timing of nondeposition, and timing of erosion. These in turn allow for improved understanding of the tectonic history of each area. GEM projects produced a vast array of products, from geological and geophysical maps and geochemical data to peer-reviewed scientific papers. Reference to the most important of these products is made in the individual papers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.301 | 0.191 |
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".