Modelling lake sediment geochemical distribution using principal component, indicator kriging and multifractal power-spectrum analysis: a case study from Gowganda, Ontario
Bibliographic record
Abstract
Combined geostatistical and multifractal power-spectrum modelling of geochemical distributions can provide suitable indicators of metal dispersion, and is capable of analysing complex problems for targeting potential areas for mineral exploration. A case study analysing lake sediment geochemical data for the Gowganda area is presented and development of the methodology for spatial analysis of the data is described. The Gowganda-Cobalt area of northeastern Ontario is a textbook example of Co, Ag-Co vein-type deposit, which by 1984 had yielded one-half billion ounces of Ag. The area is also known for shear-zone-hosted Au mineralization. This paper uses the spatial and geometric distribution of lake sediment data to discriminate geochemical anomalies from background values. The application of two geostatistical techniques (spatial principal component analysis and indicator kriging) allows the estimation of geochemical distributions by utilizing their statistical and spatial properties. The newly developed multifractal power-spectrum method additionally allows for the geochemical distributions to be modelled by their multifractal Fourier-transformed power-spectrum characteristics. Verification of the estimates produced by these techniques has been enabled through spatial analysis of bedrock geology and mineral deposit occurrences in the area.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".