Till Geochemistry as a Vector to Metasomatic Iron and Alkali-Calcic Systems and Associated Deposits in the Great Bear Magmatic Zone, Northwest Territories, Canada
Bibliographic record
Abstract
Recent advances in the characterization of metasomatic iron and alkali-calcic (MIAC) systems with associated iron-oxide apatite (IOA) prospects and iron-oxide–copper–gold (IOCG) and metasomatic cobalt deposits of the Great Bear magmatic zone were used to determine if the geochemistry of glacial sediments can unveil pathfinder elements indicative of mineralization and associated alteration. Analysis of variance within bedrock lithogeochemical (n = 707 samples) and till geochemical datasets (n = 92 samples) are compared. Results show that Fe, Co, Ni, Cu, As, Mo, Bi, La, Th, U, and W were identified as potential vectoring elements in different fractions of till due to their anomalous concentrations down-ice of various mineralized outcrops within the study area. For instance, Fe, Co, Cu, and Mo were established as the most useful vectoring elements in the locally derived till (<2 km down-ice) near the Sue Dianne IOCG deposit, and Fe, Co, Ni, Cu, Mo, W, Bi, and U near the Fab IOCG prospect. At the Sue Dianne deposit, the ratios of near-total (4-acid digestion) versus partial (modified aqua regia digestion) concentrations in the silt + clay-sized till fraction (<0.063 mm) for both La and Th reflect the mineralization alteration signature and define a more consistent dispersal train from mineralization compared to element concentrations mapped alone. Additional testing in an area of continuous till cover near an isolated point source is recommended to further develop the elemental ratio method for exploration of MIAC systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".