Matrix Corrected SIMS In Situ Oxygen Isotope Analyses of Marine Shell Aragonite for High Resolution Seawater Temperature Reconstructions
Bibliographic record
Abstract
Abstract Marine shells incorporate oxygen isotope signatures during growth, creating valuable records of seawater temperature and marine oxygen isotopic compositions. Secondary ion mass spectrometry (SIMS) measures these compositions in situ at finer length‐scales than traditional stable isotope analyses. However, determining oxygen isotope ratios in aragonite, the most common shell mineral, is hampered by a lack of ideal reference materials, limiting the accuracy of SIMS‐based seawater temperature reconstructions. Here, we tested the capability of SIMS to produce seawater temperature reconstructions despite the matrix calibration challenges associated with aragonite. We cultured Anadara trapezia bivalves at four controlled seawater temperatures (13–28°C) and used strontium labeling to mark the start of the temperature‐controlled shell increment, allowing for more spatially precise SIMS analysis. An improved matrix calibration was developed to ensure more accurate bio‐aragonite analyses that addressed matrix differences between the pure abiotic reference materials and the bio‐aragonite samples with intricate mineral‐organic architectures and distinct minor and trace element compositions. We regressed SIMS‐IRMS biases of abiotic and biogenic aragonites that account for their systematic differences in major, minor, and trace elements, allowing for more accurate SIMS analyses of the temperature‐controlled shell increment. The thorough matrix calibration allowed us to provide a SIMS‐based seawater‐corrected oxygen isotope thermometer of T(°C) = 23.05 ± 0.36 − 4.48 · (δ 18 O aragonite [‰ VPDB] − δ 18 O seawater [‰ VSMOW] ± 0.25) and 10 3 ln α aragonite‐seawater = (17.78 ± 0.88) · 10 3 /T (K) − (29.44 ± 2.40) that agrees with existing aragonitic IRMS‐based thermometer relationships and improves the applicability of SIMS‐based paleo‐environmental reconstructions of marine bio‐aragonites.
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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 | 0.001 |
| 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".