Analysis of geochemical data for mineral exploration using a GIS — A case study from the Swayze greenstone belt, northern Ontario, Canada
Bibliographic record
Abstract
Abstract Geographic Information Systems (GIS) provide the geologist with a powerful tool, when used in concert with statistical and geostatistical analysis, for archiving, manipulating, analysing and visualizing geochemical data. This paper uses geochemical (Zn, Cu) data obtained from various media (rock, lake sediments, till, soil and humus) over the Swayze greenstone belt in northern Ontario, to explore methods for analysing and visualizing geochemical data with a focus to mineral exploration applications. The behaviour of Zn and Cu in both bedrock and the surficial environment is studied using statistical and geostatistical techniques. Interpretation and uses of traditional statistics and dot plots are contrasted with interpolated geochemical maps as well as red-green-blue (RGB) ternary maps. Techniques for multimedia comparison and geochemical anomaly detection and screening are presented. The processing methods presented in this paper can be utilized and adapted by other geologists for exploring their own geochemical data. Many of the algorithms presented here are available within standard GIS software packages, or can be written easily using a GIS macro language.
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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 | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".