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Record W2153000470 · doi:10.2113/econgeo.110.6.1389

Trace Element Content of Sedimentary Pyrite in Black Shales

2015· article· en· W2153000470 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEconomic Geology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsYukon University
Fundersnot available
KeywordsPyriteGeologyTrace elementSphaleriteDiagenesisMineralogyGeochemistrySedimentary rockArsenicChemistry

Abstract

fetched live from OpenAlex

<p>Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analyses of 1,407 sedimentary (diagenetic and syngenetic) pyrites from 45 carbonaceous shale and unconsolidated sulfidic sediment samples, ranging in age from Paleoarchean to present day, show a considerable range of trace element compositions. Arsenic, Ni, Pb, Cu, and Co are among the most abundant trace elements, with medians ranging from 100 to 1,000 ppm. Less abundant elements Mo, Sb, Zn, and Se have median ranges of 10 to 100 ppm, and Ag, Bi, Te, Cd, and Au have median ranges of 0.01 to 10 ppm. Our dataset reveals three main groups of trace elements that are incorporated into pyrite in different ways. Group 1 elements (As, Ni, Co, Sb, Se, and Mo) are contained uniformly throughout the pyrite and may be held within the pyrite crystal structure or as nanoinclusions evenly distributed within pyrite. Group 2 elements (Bi, Pb, Ag, Au, Te, and Cu) generally occur uniformly at low concentrations and may be incorporated into the pyrite structure but are highly variable at high concentrations, where they may also occur as microinclusions. Group 3 elements (Zn and Cd) tend to have highly variable abundances and generally occur in pyrite as microinclusions of sphalerite. </p> <p>Factor analyses of the dataset identified five factors that account for 65.4% of the variance in pyrite trace element concentrations. Factor 1 includes Pb, Bi, Au, and Te, and explains 18.1% of the variance, possibly due to As(II) (Qian et al., 2013) or As(III) substituting for Fe in pyrite, which induces the uptake of these elements. Factor 2 includes Co, Ni, and As and accounts for 13.6% of the variance, possibly due to the presence of As(–I) substituting for S(–II) in pyrite, which, in turn, promotes the uptake of Ni and Co. Factor 3 includes Zn and Cd and explains 12.3% of the variance and is due to the presence of sphalerite inclusions. Factor 4 includes Se, Ag, and Sb and explains 11.0% of the variance, which is believed to reflect coeval input of these elements into the oceans during periods of increased oxygenation. Factor 5 includes Mn, Cu, and Mo and explains 10.4% of the variance. It is likely that this behavior is due to these elements being delivered together to the sediments by adsorbing to Mn (hydro)oxides, which are released when the Mn (hydro)oxides dissolve in reducing bottom waters or pore waters. </p> <p>Variations in pyrite texture do not show consistent compositional patterns between different samples, though within the same sample later formed pyrite tends to have lower trace element abundance. Many trace elements associated with mafic extrusions/circulation of fluids through mafic rocks (Ni, Co) are more enriched in Archean sedimentary pyrite at times when mafic volcanism/circulation of fluids through mafic rocks was more active. Similarly, some trace elements tend to be more enriched in Phanerozoic pyrite due to increasing levels of atmospheric oxidation.</p>

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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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
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)0.0130.001

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 head0.042
GPT teacher head0.208
Teacher spread0.166 · 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