Chemical, mineralogical and isotopic characterization of Ca-Mg-Fe carbonates for SIMS microanalysis
Bibliographic record
Abstract
Carbonate minerals of the dolomite-ankerite and magnesite-siderite series are often found in sedimentary basins associated with economically viable ore deposits and as alteration product of Ca-Mg-Fe-silicates in igneous and metamorphic rocks. Analysis of oxygen and carbon isotopes in such carbonates gives important information, among others, on their evolution and spatial distribution during sediment burial and diagenesis, crystallization temperature during sedimentation, diagenesis and hydrothermal alteration, fluid and carbon sources, mechanisms of CO2 sequestration (e.g., (Śliwiński et al., 2016, 2018 and references therein). Because of their common chemical zoning at the microscale, in-situ techniques such as Secondary Ion Mass Spectrometry (SIMS) are fundamental to unravel intragrain and intergrain isotopic heterogeneities at scales < 50 µm. Due to instrumental artifacts, SIMS analyses need to be calibrated with matrix-matched reference materials to be accurate. This dataset describes a newly compiled set of Ca-Mg-Fe carbonates that were characterized for their mineralogical (XRD), major and minor element chemical composition (EPMA), oxygen and carbon isotopic composition by acid digestion gas-source isotope ratio mass spectrometry (GS-IRMS), and oxygen and carbon isotopic homogeneity at the microscale (SIMS). Three dolomites and one ankerite with Fe# (molar Fe/(Fe+Mg)) ranging from 0.0004 to 0.3429, one magnesite (Fe# = 0.0099) and one siderite (Fe# = 0.6152) are now available for the global SIMS community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".