Mineralogical controls on Li, Sr and oxygen isotope composition of mixed Ca Mg carbonate phases with implications for sedimentary dolomites
Bibliographic record
Abstract
The formation of ordered dolomite is unlikely to occur under ambient Earth's surface conditions, yet “disordered dolomite,” has been shown to crystallize at temperatures as low as 40 °C. Such synthetic precipitates have a similar d (104) spacing as dolomite, and have been studied previously to determine their O isotope compositions as a function of temperature with the goal of using oxygen isotopes as a temperature proxy. However, laboratory synthesis yields mineralogical assemblages that transform to more stable phase assemblages over time. Previous studies however have not thoroughly addressed how this transformation proceeds and how it affects O isotope compositions of the precipitates. To better understand the relationship between temperature and δ 18 O values of Ca-Mg‑carbonates at temperatures <100 °C, in this study, Ca Mg carbonates were synthesized at 40, 60 and 80 °C and incubated up to 104 days. Mineralogical composition was quantified using Rietveld refinement of X-ray diffraction patterns, while concomitantly monitoring fluid and solid compositions to assess the utility of the δ 18 O solid , Li, and Sr compositions as paleo-proxies in complex Ca-Mg‑carbonate assemblages. The results suggest a continuous transformation of the mineralogy of the samples throughout the duration of the experimental runs, although Mg/Ca of the bulk solids remained quasi-constant at ∼1, and between 40 and 104 days of reaction the bulk δ 18 O solid values did not exhibit significant variations. These δ 18 O solid values were used to estimate temperature-dependent oxygen isotope fractionation between bulk solid and fluid that can be expressed as: 10 3 ln α solid − fluid = 1.78 ± 0.13 10 6 T 2 + 8.47 ± 1.20 where T is temperature in Kelvin. The use of this relation yields significantly different temperature dependence compared to that reported earlier by Schmidt et al. (2005) using the same synthesis procedure. The difference can be assigned to mineralogical changes occurring in the precipitates over the course of the 104-day runs that may not have occurred in the earlier study owing to a shorter experimental duration. Here we discuss in detail the role mineralogy has on the chemical and isotopic compositions of Ca Mg carbonates, and the implications for using Ca Mg carbonate minerals as paleoarchives.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".