The interpretation of background variation in regional geochemical surveys – an example from Nunavut, Canada
Bibliographic record
Abstract
In glaciated terrains, geochemical maps portray bedrock provenance and the integrated effects of glacial processes affecting the distribution, concentration, and partitioning of minerals in sediments. In those maps, indicators of economic mineralization can be difficult to distinguish in the context of natural background, especially at low trace metal concentrations. Sample mineralogy, which can be inferred from lithophile elements, provides a key basis for interpreting sediment provenance and controls on background variation in metal concentrations. In non-carbonate terrain, the principal rock-forming minerals digested by aqua regia are Mg-bearing phyllosilicates, including trioctohedral chlorite, biotite, and phlogopite. Hence, Mg analyses directly reflect the concentrations of those minerals. In a regional geochemical survey of till in Nunavut, strong linear correlations ( r >0.840, n =1842, p <0.0001) for Cu and Cr with Mg concentrations indicate Mg-bearing phyllosilicates are the principal metal hosts, and that the metals are bound in mineral lattice structures in direct proportion to Mg. Thus, metal:Mg ratios express geochemical properties of the mineral(s) hosting the metal, and are independent of mineral partitioning among size fractions that results from either glacial or postglacial processes. Ratio maps can be used to establish till provenance and infer aspects of bedrock composition not evident in single-element geochemical maps. Ratio anomalies could indicate metals derived from economic indicators such as sulphide minerals, and metal-rich particulate from anthropogenic sources .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".