Trace element variation in ore minerals from the Iron Cap deposit (KSM), British Columbia, Canada: Implications for fluid evolution in porphyry-epithermal gold systems
Bibliographic record
Abstract
Trace amounts of elements such as As, Sb,Se, Te, Pd, Hg and Au are commonly identified in porphyry and epithermal mineral deposits. Investigating variations in the concentration, distribution and hosting of these elements in an ore mineral suite has the potential to enable elucidation of the specific characteristics of discrete fluids throughout the evolution of a mineralizing magmatic-hydrothermal system. The Iron Cap deposit in the Kerr-Sulphurets-Mitchell (KSM) district, British Columbia, Canada, provides an opportunity to interpret the characteristics of hydrothermal fluids from an early porphyry stage, through transition, to a late epithermal stage. A suite of 60 core samples are characterized by petrographic methods to constrain the sequence of vein formation at Iron Cap, and identify deposit mineralogy. The results of initial trace element analysis of ore minerals by SEM-EDX analysis has shown that pyrite growth zones are Cu-or As-bearing, and arsenopyrite growth zones have a variable Fe:As:S ratio. Galena commonly contains Se, ranging in concentration from ~2wt. % to clausthalite (PbSe). Additionally, gold in the deposit contains between 2-42 wt. % Ag. These initial discoveries will allow a methodology to be developed to interpret fluid conditions with trace element variations.
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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 | 0.003 | 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".