Contamination of mafic to ultramafic magmas by sulfur-bearing sediments: evaluation of the environment of deposition and tracing the unique signature of the contaminants through the magma using multiple sulfur and iron isotope data
Bibliographic record
Abstract
Multiple stable isotopes of sulfur (δ33S, δ34S, and Δ33S) and iron (δ56Fe) are used to identify the sources of sedimentary rock contamination at both the Voisey’s Bay (Labrador, Canada) and the Hart (Ontario, Canada) magmatic Ni-Cu-platinum-group element (PGE) deposits. At both locations, sulfide minerals were formed in sedimentary rocks during diagenesis as a result of bacterial sulfate reduction, prior to interaction with the magmas that resulted in the formation of the sulfide mineralization. In the Hart area, both exhalite and graphitic argillite were formed under predominantly anoxic conditions with localized, or transient, oxygen oases in seawater. The fluid composition was a result of mixing of seawater with hydrothermal fluids. Sulfur in the sediments in the Hart area was derived from the reduction of sulfate that had been mass-independently fractionated in the anoxic Archean atmosphere prior to delivery to the seawater. Multiple sulfur isotopes identified the sources of contamination in both the Voisey’s Bay and Hart deposits, and determined that the Main Zone and Eastern Extension at Hart likely had different contaminants that provided sulfur to form the mineralization. Signatures of these contaminants were distinguishable up to a few hundred meters from the sulfide-rich zones, allowing this to be used as a geochemical tool to vector towards the mineralization. The iron isotopic composition of sulfides from the Voisey’s Bay deposit was too heavily influenced by the host silicate magma to recognize the signature of contamination, but could be used to identify contamination in the Hart deposit. However, this data does not uniquely identify the source of contamination in the Hart deposit, and is not able to identify the signature of contamination at distances of more than a few meters from sulfide mineralization. These data sets have different sensitivity to contamination during equilibrium isotope exchanges with the silicate magma due to the difference in the initial concentration in the magma, as indicated the difference in the distance away from sulfide mineralization at which contamination can still be recognized. In conclusion, based on this study, use of multiple isotope and elemental methods to determine the presence and extent of contamination is strongly recommended.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".