Evaluation of partial digestions for soils to detect a deeply buried VMS Cu-Zn prospect in boreal forests
Bibliographic record
Abstract
Eleven partial extraction methods were assessed for their exploration utility on 15 soil samples from a one-kilometre transect over the Palaeoproterozoic Talbot VMS occurrence that is hosted by metamorphic sequences of the Flin Flon-Snow Lake terrane and overlain by 100 m of Palaeozoic dolomites and up to 2 m of Quaternary glacial sediments. Student’s t- test statistics and t- distribution probabilities (P t ) were calculated to evaluate the magnitude and spatial accuracy of anomalies in each dataset. In the surface soil that was analysed, Zn anomalies have the greatest contrast by most methods, followed by P and Cd, albeit mostly restricted to a strong anomaly at a fault zone that is laterally offset c. 100 m from the projection of mineralization. Extraction methods with significant anomaly contrast in decreasing order of exploration utility are Enzyme Leach, deionized water, sodium pyrophosphate, MMI, and ammonium acetate at pH 7. Weaker leaches generate anomalies with greater contrast than stronger acid digests of the soils, which tend to dissolve the sample matrix and overwhelm the secondary signal from adsorbed species of ore indicator elements. Aqua regia digestions on the clay fraction extracted from the soil also have greater contrast than most soil digestions for ore-indicator elements. Continuous-leach ICP-MS analysis of the Talbot soil provides detailed phase relationships that indicate the presence of labile Zn present as organic complexes in anomalous soil over the fault, and the absence of such secondary Zn species in background soil, in which most Zn is released from carbonate dissolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".