Classification and origin of vein-related Cu-Ag-Pb-Zn mineralization at Salkeld Lake, Nonacho Basin, Northwest Territories, Canada
Bibliographic record
Abstract
The Paleoproterozoic Nonacho Basin, Northwest Territories, Canada, contains a variety of poorly characterized mineral deposits, including hydrothermal Cu-Ag-(Pb-Zn) \nmineralization with one of the most representative examples at Salkeld Lake. The \nmineralization occurs in breccias, veins and disseminations within fracture networks and \nshear zones, cross-cutting granitic gneiss, metasedimentary rocks and alkali granitoids. \nUtilizing a variety of bulk and microanalytical methods, this study provides the first genetic \nconstraints for, and a classification of, the mineralization at Salkeld Lake. \nThe results of fluid inclusion and stable O isotope analyses of quartz and calcite \nsuggest mixing, dilution and oxidation of a carbonic-aqueous mineralizing fluid by the \nintroduction of cooler meteoric water, which led to metal precipitation. End-stage \nmineralizing conditions were at near surface P and T ~150-180 oC. The Cu-Ag-(Pb-Zn) \nmineralization formed at ~1831 ± 2 Ma (laser Ar-Ar of pre- to syn-mineralization \nmuscovite) and is classified as an intrusion-related, sediment-hosted vein- and breccia related Cu-Ag-(Pb-Zn) deposit. Possible heat and metal sources are the calc-alkalic mafic \nSparrow Dykes or related magmatic events, timed with the waning stages of Trans Hudson Orogen (~2.0-1.8 Ga).
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".