Understanding Copper Isotope Behavior in the High Temperature Magmatic‐Hydrothermal Porphyry Environment
Bibliographic record
Abstract
Abstract Copper stable isotope geochemistry has the potential to constrain aspects of ore deposit formation once variations in the isotopic data can be related to the physiochemical conditions during metal deposition. This study presents Cu isotope ratios for samples from the Pebble porphyry Cu‐Au‐Mo deposit in Alaska. The δ65Cu values for hypogene copper sulfides range from −2.09‰ to 1.11‰ and show linear correlations with the δ18O isotope ratios calculated for the fluid in equilibrium with the hydrothermal alteration minerals in each sample. Samples with sodic‐potassic, potassic, and illite alteration display a negative linear correlation between the Cu and O isotope results. This suggests that fractionation of Cu isotopes between the fluid and precipitating chalcopyrite is positive as the hydrothermal fluid is evolving from magmatic to mixed magmatic‐meteoric compositions. Samples with advanced argillic alteration display a weak positive linear correlation between Cu and O isotope results consistent with small negative fluid‐chalcopyrite Cu isotope fractionation during fluid evolution. The hydrothermal fluids that formed sodic‐potassic, potassic, and illite alteration likely transported Cu as CuHS0. Hydrothermal fluids that resulted in advanced argillic alteration likely transport Cu as . The pH conditions also control Cu isotope fractionation, consistent with previous experimental work. Larger fractionation factors were found between fluids and chalcopyrite precipitating under neutral conditions contrasting with small fractionation factors calculated between fluids and chalcopyrite precipitating under acidic conditions. Therefore, this study proposes that hydrothermal fluid compositions and pH conditions are related to Cu isotope variations in high temperature magmatic‐hydrothermal deposits.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".