Khan River and Bear Lake: Two Natural Titanite Reference Materials for High‐Spatial Resolution U‐Pb Microanalysis
Bibliographic record
Abstract
The Khan River (Namibia) and Bear Lake (Canada) titanites are investigated as potential reference materials (RM) for LA‐ICP‐MS applications. The Bear Lake titanite is texturally and compositionally homogeneous. The Khan River titanite is texturally heterogeneous and characterised by variable trace element compositions and total rare earth element contents. However, both titanites have consistent U‐Pb and Nd‐isotope ratios. U‐Pb isotope dilution‐thermal ionisation mass spectrometry analyses yielded Pb c ‐uncorrected intercept ages of 516.3 ± 1.3 Ma (2 s , n = 5, MSWD = 2.4) and 1067.81 ± 0.74 Ma (2 s , n = 4, MSWD = 0.35) for Khan River and Bear Lake titanites, respectively. Multiple U‐Pb LA‐SF/MC‐ICP‐MS analyses gave consistent Pb c ‐uncorrected intercept ages for both, Khan River (517 ± 1/5 Ma, 2 s , n = 262, MSWD = 1.5) and Bear Lake (1070 ± 1/11 Ma, 2 s , n = 325, MSWD = 0.88). U‐Pb SHRIMP analyses on the same material returned identical (within uncertainty) ages. Khan River and Bear Lake gave internally consistent solution MC‐ICP‐MS 143 Nd/ 144 Nd ratios of 0.511587 ± 0.000027 (2 s , n = 2) and 0.512321 ± 0.000004 (2 s , n = 2), respectively. The 143 Nd/ 144 Nd ratios via solution‐MC‐ICP‐MS and LA‐ICP‐MS all agree within uncertainty and suggest that both titanites can be used as RMs for Nd‐isotope analyses.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".