Evaluation of magnetite as an indicator mineral for porphyry Cu exploration: a case study using bedrock and stream sediments at the Casino porphyry Cu–Au–Mo deposit, Yukon, Canada
Bibliographic record
Abstract
The trace element composition of detrital magnetite grains recovered from six local streams around the Casino high-grade porphyry Cu–Au–Mo deposit, west-central Yukon, is compared with igneous and magmatic-hydrothermal magnetite recovered from mineralized and unmineralized host rocks at the deposit. Linear discriminant analysis of 12 elements (Mg, Al, Ti, V, Mn, Co, Cr, Ni, Cu, Zn, Ga and Ge) and plots of Ti v. Ni/Cr are used to discriminate between magmatic-hydrothermal magnetite from the potassic alteration zone and igneous magnetite from granodiorite and quartz monzonite hosting the deposit. Magmatic-hydrothermal magnetite with a trace element composition similar to that from the potassic alteration zone at Casino is identifiable in stream sediments draining the deposit. Copper in magmatic-hydrothermal magnetite, present as minute inclusions of sulfide minerals such as chalcopyrite or substituted within the magnetite crystal lattice, is a strong indicator of Cu mineralization. We show that the chemical compositions of magnetite recovered from stream sediments can be used to explore for porphyry systems. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis Supplementary material: Laser ablation data for major, minor and trace elements in magnetite from bedrock and stream sediment samples from Casino are available at https://doi.org/10.6084/m9.figshare.c.5896900
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".