Trace element distribution in primary sulfides and Fe–Ti oxides from the \nsulfide-rich pods of the Lac des Iles Pd deposits, Western Ontario, Canada : \nConstraints on processes controlling the composition of the ore and the use of \npentlandite compositions in exploration
Bibliographic record
Abstract
There is an on-going debate as to whether the Lac des Iles Pd deposits (Ontario, Canada) are of magmatic or hydrothermal origin. An aspect of the deposits that has not yet been documented is the presence of sulfide-rich pods which occur throughout the host intrusion (the Mine Block Intrusion). The ore mineralogy of the sulfide-rich pods consists of pyrrhotite, pentlandite, chalcopyrite, ± pyrite, magnetite and ilmenite. We present the trace element concentrations of pyrrhotite, pentlandite, chalcopyrite, magnetite, and ilmenite from the pods and compare these results with results from other Ni–Cu–platinum-group element (PGE) deposits. The low concentrations of Si and Ca and high concentrations of V, Ni, and Cr in magnetite are consistent with a magmatic origin of the magnetite. Variations in the V and Cr concentrations indicate that magnetite crystallized from a magmatic sulfide liquid during crystal fractionation of the sulfide liquid. The enrichments in Ni, Co, Os, Ir, Ru, and Rh and depletions in Cu, Ag, Cd, and Zn in pentlandite and pyrrhotite relative to chalcopyrite are also consistent with the formation of the pods by crystallization of a magmatic sulfide liquid. Comparison of pyrrhotite and pentlandite compositions from Lac des Iles with those from other Ni–Cu–PGE deposits shows that pyrrhotite and pentlandite derived from evolved magmas have distinct compositions relative to those derived from more primitive magmas. In addition, this comparison shows that pentlandites from PGE-dominated deposits are richer in Pd and Rh than pentlandites from Ni–Cu sulfide deposits. A plot of Pd vs Rh appears to be effective at distinguishing pentlandites of PGE-dominated deposits from those of Ni–Cu sulfide deposits and could possibly be used to adapt exploration strategies.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".