Dataset of geochemical data from iron oxide alkali-altered mineralising systems of the Great Bear magmatic zone, Northwest Territories
Bibliographic record
Abstract
The Great Bear magmatic zone is a Paleoproterozoic calc-alkaline to shoshonitic volcano-plutonic belt in the Northwest Territories of Canada. The region hosts regional-scale iron oxide and alkali-alteration mineral systems with polymetallic iron oxide copper-gold (IOCG) deposits and prospects as well as iron oxide±apatite, skarn and albitite-hosted uranium mineralisation. In the course of phase-three Targeted Geoscience Mapping program and phase-one Geo-mapping for Energy and Minerals program as well as in 1973 for the Bear Province lithogeochemical survey project, a total of 1720 samples were collected across these iron oxide alkali alteration systems between 2005 and 2012 and their chemical composition analysed by complementary methods. In this Open File, we report over 3500 lithogeochemical analyses (including analyses from distinct laboratories, duplicates and standards). In these samples, metals above the NORMIN cut-off grades for mineral showings include base, precious, strategic and specialised metals including rare-earth elements as well as uranium and thorium. The format chosen for the data set is tailored for geographic information systems (GIS); all samples are georeferenced and as such the data set locates all the anomalous metal concentrations encountered during the aforementioned projects. Metadata is being provided on the sampling protocols, the analytical methods, and the quality control results. This complete data set of analyses is also being published to serve as an electronic supplement to current and future scientific publications associated with these projects as well as a support for a subsequent open file where the best results per elements will be compiled. As such, this dataset provides the documentation for the commonalities observed in the chemical fingerprints of each hydrothermal alteration facies across the varied systems and anchor the refined lithogeochemical exploration methods developed for IOCG and affiliated deposits. In addition, the dataset can be used as baseline knowledge for the natural geological environment of the belt that guides the natural metal distribution in glacial sediments, soils, water and biomass.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".