A Machine Learning Approach to Single Garnet Geothermometry and Application to Tracing the Fingerprint of Superdeep Diamonds
Bibliographic record
Abstract
Abstract Estimating the equilibration temperatures of mantle‐derived garnets is crucial for assessing the diamond potential of kimberlites. Traditional garnet geothermometers require co‐existing mineral data or costly trace element analysis, limiting their practical use. As an alternative approach, based on the major and minor element composition of garnet alone, we first re‐calibrated an Mn‐in‐garnet thermometer using a newly compiled data set of garnets from well‐equilibrated peridotitic xenoliths with well‐constrained pressure‐temperature (P‐T) conditions. The re‐calibrated Mn‐in‐garnet thermometer, however, is only of intermediate accuracy, with a relatively large discrepancy relative to the most reliable multi‐phase thermometry, indicated by a high root mean square error value (RMSE = 79°C) across a temperature range from 900 to 1,400°C. In a second improve approach, we developed a new machine learning (ML)‐based garnet thermometer that demonstrated superior performance, achieving significantly better accuracy and reduced discrepancies (average RMSE = 61°C). The ML‐based garnet thermometer outperforms the Mn‐in‐garnet thermometer because it considers not only MnO but also other major and minor elements, particularly TiO2, revealed by the ML model to be critical for accurate prediction of garnet temperatures. Applying the ML‐based thermometer to garnet xenocrysts from kimberlites on the Slave and Kaapvaal cratons reveals that high numbers of sublithospheric (superdeep) diamonds are associated with significantly higher proportions of high‐T (>1,200°C) high‐Ti garnets, compared to kimberlites in which superdeep diamonds are either few or absent. This finding indicates that a number of kimberlites, not currently identified as containing superdeep diamond populations, are promising hosts of such diamonds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".