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Record W2948469130 · doi:10.4095/296694

Trace element distribution in sulphide assemblages of the Levack-Morrison ore system, Sudbury, Ontario: looking for chemical fingerprints of mineralization processes

2015· report· en· W2948469130 on OpenAlexaffabout
M Adibpour, Pedro J. Jugo, D E Ames

Bibliographic record

Venuenot available
Typereport
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsTrace elementMineralization (soil science)GeologyGeochemistryMineralogyTRACE (psycholinguistics)ArchaeologyGeographySoil science

Abstract

fetched live from OpenAlex

One challenge in the exploration for Cu-Ni-PGE mineralization in the footwall of the Sudbury Igneous Complex (SIC) is the uncertainty of its origin. The relative proximity of mineralization to the SIC is consistent with models of magmatic fractionation, but the common association of ore in the SIC footwall with amphibole and epidote alteration is consistent with a hydrothermal origin. Although these processes are not mutually exclusive (e.g. ores of magmatic origin could have been later remobilized by hydrothermal fluids), better constraints on which processes operated would greatly assist exploration. This project is a pilot study to assess whether chemical fingerprints can be established for four distinct mineralization types in the Levack-Morrison ore system: (a) contact; (b) a transition zone between contact and footwall ore; (c) sharpwalled veins; and (d) disseminated, S-poor, PGE-rich ores. Sulphide assemblages consisting primarily of pyrrhotite, chalcopyrite, and pentlandite were characterized in detail (petrography, SEM, EPMA, LA-ICP-MS). The results indicate that (a) Se content increases with depth; and (b) some trace elements (e.g. Cd vs. Se in chalcopyrite, Co vs. Se in pentlandite and pyrrhotite) can discriminate among different ore types. Calculated partition coefficients (± 2?) for Se in chalcopyrite and pentlandite (1.2 ± 0.1 for contact and transition ores, 0.5 ± 0.2 for sharp-walled veins) are significantly different, which is consistent with different mineralization processes for those ore types. In addition to trace element content calculation in major sulphides, element distribution maps were created from LA-ICP-MS spectra of sulphide assemblages. Some contact-style samples contained abundant euhedral pyrite but pyrite was also present in samples of other ore types. The maps showed complex trace element zonation (e.g. Se, Co, and As) in pyrite in contact ore, as well as some PGE minerals (notably Ir and Os). In contrast, no PGEs were detected in any of the other sulphides or any compositional zoning. Because Ir has very low solubility under most hydrothermal conditions, Co-rich, Ir-bearing pyrite was interpreted to have formed from the cooling of a sulphur-rich sulphide liquid. Such pyrite (when present) could be used as an indicator of a magmatic signature. To further refine these results, future work would need to focus on three areas: (1) analyses of additional samples from the Morrison-Levack ore system to validate the discrimination diagrams for different ore types; (2) similar work would need to be undertaken elsewhere in the Sudbury mining district, to establish if the proposed discrimination plots are applicable basin-wide; (3) better constraints would need to be established for the origin of the Co-rich, PGE-bearing pyrite to enable it to be used as a marker of ore type.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.278
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes2
Has abstractyes

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