The glacial transport and physical partitioning of mercury and gold in till: implications for mineral exploration with examples from central British Columbia, Canada
Bibliographic record
Abstract
Abstract Mercury glacial dispersal was measured in the clay-sized fraction (< 0.002 mm) and heavy mineral concentrate (0.063–0.250 mm, specific gravity > 3.3 g/cm 3 ) of till in a region of bedrock cinnabar occurrences, in central British Columbia, Canada. Most of the Hg in till occurs as sand-sized cinnabar (HgS) grains. A longer dispersal train was measured with the heavy mineral concentrates because Hg concentrations in heavy minerals yielded a higher ratio between anomalous and background concentrations when compared to the clay-sized material. It is proposed that geochemical or mineralogical analyses on a specific grain size fraction or density fraction of till, where the desired metal resides, result in a higher contrast between anomalous and background concentrations. Such a great contrast translates into a longer detectable dispersal train and hence, a larger target for mineral exploration. Therefore, in drift exploration programs, it is crucial to identify the mode of occurrence of a sought commodity in till; this can be achieved in part with a simple partitioning study whereby metal concentrations are measured in specific grain size fractions of till. Physical partitioning results for Au in the study area indicate that close to the bedrock source, large metal concentrations in some cases are present in the sand- (0.063–2 mm) and granule-sized (2–4 mm) fractions. Therefore, the significance of a regional Au anomaly, commonly defined in the silt plus clay-sized fraction of till could be evaluated by further determining the Au content of coarser size fractions (sand and granule).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".