Use of surficial geochemical methods to locate areas of buried uranium mineralization in the Jacque’s Lake area of the Central Mineral Belt, Labrador, Canada
Bibliographic record
Abstract
Surficial geochemical methods were applied to delineate zones of anomalous uranium and related element concentrations in areas overlain by an extensive blanket of glacial sediments and dense vegetation cover in the Jacque’s Lake area of the Central Mineral Belt, Labrador, Canada. The study involved sampling and analyses of vegetation including black spruce twigs and bark, Labrador tea shoots, and humus. Ash derived from the vegetation samples was analyzed using inductively coupled plasma – mass spectrometry (ICP–MS) following ignition at 450 °C, and humus was analyzed with delayed neutron counting (DNC) for a suite of 35 elements. B-horizon soil was analyzed using aqua regia digestion to investigate potential chemical signatures of bedrock mineralization at the surface. Uranium concentration in humus varied from 0.05 to 885 ppm. Zones exhibiting anomalous U responses were associated with areas proximal to anoxic peat and sphagnum bogs where mobile U species were sequestered. Uranium and pathfinder element (e.g., Pb, V, Sr, and Mo) concentrations were low in the <250 μm fraction of B-horizon soils. The soil geochemistry delineated bedrock U mineralization in areas with <15 cm of overburden and U concentration varied from 50 to 405 ppm. Biogeochemical signatures of the bedrock mineralization in black spruce twigs produced greater anomaly to background contrasts for U and pathfinder elements (e.g., Be, Ag, Pb, Ca, and Sb) and correlated more precisely with the detected radiometric U/Th anomaly than did those of soil and humus. Principal component analysis of spruce twig data discriminated three major components, including plant nutrients, ore-related elements, and a mobile species. Uranium concentration varied from below detection limit to 23 ppm U in black spruce bark and from below detection limit in ∼40% of samples to 18 ppm in Labrador tea stem.
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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 | 0.003 | 0.002 |
| 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) | 0.001 | 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".