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Record W2951332731 · doi:10.2110/jsr.2019.29

A New Approach To Quantify the Ordering State of Protodolomite Using XRD, TEM, and Z-Contrast Imaging

2019· article· en· W2951332731 on OpenAlexaboutno aff
Yihang Fang, Huifang Xu

Bibliographic record

VenueJournal of Sedimentary Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsnot available
Fundersnot available
KeywordsContrast (vision)GeologyMineralogyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Abstract Dolomite, an ordered Ca-Mg-carbonate mineral, is abundant in the sedimentary record but found only rarely found in Holocene and modern marine settings. Instead, protodolomite, a partially ordered Ca-Mg-carbonate with a composition close to ideal dolomite, and disordered dolomite occur in specific modern sedimentary settings. In this study, the protodolomite in a late Holocene stromatolite collected from Manito Lake, Saskatchewan, Canada, was examined using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Z-contrast imaging from scanning-transmission electron microscopy (STEM). The protodolomite is characterized by nano-domains exhibiting a weak to moderate degree of Ca-Mg ordering based on attenuated and diffuse “b” reflections in selected-area diffraction patterns. The stromatolite also contains disordered dolomite that lack “b” reflections. Using Z-contrast images and image simulations, a quantitative approach was developed to calculate and constrain the ordering state of protodolomite, a parameter that is generally difficult to determine. With ordering contour lines constructed from this study, the ordering state of a weakly ordered dolomite can be quantified based on its d104 value and composition.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.312
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.063
GPT teacher head0.324
Teacher spread0.260 · 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.

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

Citations40
Published2019
Admission routes1
Has abstractyes

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