Selective leach geochemistry of soils overlying the 95-2, B30 and A4 kimberlites, northeastern Ontario
Bibliographic record
Abstract
Studies were carried out by the Geological Survey of Canada and the Ontario Geological Survey at the B30, A4 and 95-2 kimberlites in the glaciated terrain of northeastern Ontario, Canada to evaluate the effectiveness of selective leaches for detecting soil geochemical signatures over deeply (>30 m) buried kimberlites. In this region, Late Jurassic kimberlites were covered by up to 60 m of glacial sediments during the Late Wisconsin glaciation and since the region was deglaciated approximately 9000 years ago, soil-forming processes have been active. Two selective leaches, ammonium acetate pH 5 (AA5) and Mobile Metal Ions (MMI-D-commercial leach), were used along with aqua regia to detect and evaluate the geochemical signatures in mineral and organic soils over the kimberlites. Sampling was carried out using a depth-based protocol of 10 to 20 cm, regardless of mineral soil horizon sampled. The strongest "kimberlitic" responses were detected over the 95-2 kimberlite, where mineral soils were most consistent and free of organic matter. Kimberlite pathfinder elements for selective leaches applied to mineral soils will depend on the geochemical contrast between the kimberlite and its host rock. In this study over three kimberlites, pathfinder elements include: elevated concentrations of Ba, Co, Mg, Mn, Pb, REE, Sr, Ti, Ca, Ga, Cd, I Na, and U and depleted concentrations of Al, Cr, Fe, Rb, Ti and Th.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".