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Record W2804911304

Mo isotope variations in molybdenites at single-crystal, ore deposit, and global scales: Implications for Mo source fluid, transport, fractionation mechanisms, and molybdenite mineralization

2018· dissertation· en· W2804911304 on OpenAlexfundaboutno aff
Alysa Segato

Bibliographic record

VenueUWSpace (University of Waterloo) · 2018
Typedissertation
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsnot available
FundersUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaArizona State UniversityW. M. Keck Foundation
KeywordsMolybdeniteMineralization (soil science)FractionationGeochemistryGeologyIsotopeMolybdenumIsotope fractionationStable isotope ratioFluid inclusionsMineralogyChemistryMetallurgyMaterials scienceSeismologyHydrothermal circulationSoil science
DOInot available

Abstract

fetched live from OpenAlex

In this study, the Mo isotopic composition (δ98Mo) of molybdenite from 29 hand samples from various ore deposit types was analyzed. This data was compiled with data from the literature and all data (n = 420) was reported relative to international standard NIST SRM 3134 = 0.25‰ for comparison. Using this larger dataset, the range of δ98Mo in porphyry deposits is greater, and it was determined that the δ98Mo of molybdenite cannot be used to fingerprint the age of a deposit or the deposit type. Higher temperature deposit types (granite = 0.10‰, n = 25, 2SD = 1.03‰; porphyry = 0.20‰, n = 243, 2SD = 1.01‰; skarn = 0.36‰, n = 42, 2SD = 0.70‰) have generally lower δ98Mo than lower temperature deposit types (pegmatites = 0.48‰, n = 80, 2SD = 1.05‰; perigranitic = 0.75‰, n = 10, 2SD = 1.12‰; greisen = 0.79‰, n = 6, 2SD = 1.93‰), consistent with findings from earlier molybdenite δ98Mo compilations. Therefore, temperature can be considered as one control on Mo isotopic composition. The average δ98Mo of molybdenite is 0.37‰ (n = 479; 2SD = 1.30‰), which is similar to a recently estimated maximum δ98Mo for the upper continental crust of 0.40‰ and likely represents a maximum for the average bulk continental crust δ98Mo.
\nThe δ98Mo of the molybdenite samples from various deposits was compared with Re concentrations and S isotope compositions (δ34S). Consistent with earlier compilations based on a smaller dataset, an overall negative correlation was found between δ98Mo and Re concentration, which implies that the Mo source fluid is another important control on the Mo isotopic composition. Samples with a high Re concentration and a low δ98Mo suggest a mantle-derived source fluid whereas samples with high δ98Mo (>1.5‰) had uniformly low Re concentrations that suggests a crustal-sourced fluid. The relationship between δ98Mo and δ34S was also investigated as a positive correlation between these isotope systems in ore-forming systems with limited S and Mo availability would indicate Rayleigh distillation as a main mechanism of Mo isotope fractionation. No such relationship was observed, indicating that other fractionation mechanisms such as redox changes are important.
\nTo test the hypothesis of small scale zoning, molybdenite grains were cut parallel and/or across cleavage planes and analyzed. The variation observed at the single-crystal scale was within the long-term reproducibility of Mo isotope analyses (~0.1‰; 2SD = 0.2‰). Several hand samples were collected from the Berg epithermal-porphyry deposit (British Columbia) and the Hemlo disseminated Au deposit (Ontario) to quantify Mo isotopic variation at the deposit scale. At the Berg deposit, modest variation in δ98Mo was observed (~1‰). At the Hemlo deposit, Mo isotope fractionation exceeded 5‰, which is greater than the range previously reported for any other deposit type and indicates significant Mo remobilization. The mineral assemblages, trace element composition, and the abundance of pyrite in the Hemlo hand samples do not correlate with the Mo isotopic composition of bulk samples. The observed Mo isotope fractionation is likely due to alteration of the host rocks by S-rich reducing fluids.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations1
Published2018
Admission routes2
Has abstractyes

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