Abstract: Carbon dioxide in mafic magmatic systems: an experimental study to test the importance of CO2 in the formation of magmatic sulphide deposits
Bibliographic record
Abstract
Experiments are being undertaken to determine if carbonic fluids (carbon dioxide-rich, water-poor) are capable of dissolving and transporting transition ore metals (Ni, Cu) at low temperature and pressure (200–400°C and 250–400 bar). The experiments involve reacting a fluid phase (water, pure CO2, water-CO2 mixtures) with the minerals chalcopyrite and pentlandite, as well as the pure metals nickel (Ni) copper (Cu), in large volume hydrothermal autoclaves with a particular aim of understanding and quantifying how CO2-rich fluid react with and dissolve each mineral/metal. Solubility data will be collected via (i) a qualitative study of the extent and style of dissolution features on the surface of each mineral and metal (comparison made before and after the experiments) using scanning electron microscopy and laser confocal scanning microscopy, and (ii) a laser ablation ICP-MS analysis of synthetic fluid inclusions trapped in quartz during the experiments. This data will aid in developing an understanding how such fluids influenced the metal tenor and sulphide textures in mafic-ultramafic magmatic Ni-Cu sulphide deposits where carbonic fluids have been reported as a magmatic volatile phase (e.g., Lac des Iles, Ontario; Bushveld Complex, South Africa; Stillwater Complex, USA; Sudbury, Ontario).
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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 | 0.001 | 0.001 |
| 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".