Ferric Iron in CaTiO3 Perovskite as an Oxygen Barometer for Kimberlite Magmas II: Applications
Bibliographic record
Abstract
We apply an oxygen barometer based on the Fe content of CaTiO3 perovskite to estimate the oxygen fugacity (fO2) during the crystallization and emplacement of kimberlites in different eruptive phases of a single pipe, or between different pipes, clusters or provinces. Mineral chemical data for perovskite were compiled from the literature and obtained in our detailed study of perovskites from 11 kimberlites at Somerset Island and Lac de Gras, Canada. Perovskite compositions in kimberlites record a range in fO2 of many orders of magnitude from NNO 5 to NNOþ 6 [where log fO2 is given relative to the nickel^nickel oxide (NNO) buffer]. The range of fO2 recorded by different parageneses of perovskite within a single pipe can vary up to three orders of magnitude with trends toward both oxidation and reduction during crystallization. Kimberlites record some of the greatest ranges, and the highest known fO2 conditions for any terrestrial magma. This is attributed to the presence of deep and oxidized source regions and the variable interplay of ferric^ferrous vs carbon^fluid equilibria during ascent of kimberlite magmas. Three kimberlite pipes from the Lac de Gras field show that higher fO2 values correlate with higher proportions of more resorbed diamonds, suggesting that this variable has a measurable effect on the physical properties of diamonds in a pipe.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".