Calibration, Validation and Evaluation of Machine Learning Thermobarometers in Metamorphic Petrology: An Application to Biotite and Outlook for Future Strategy
Bibliographic record
Abstract
ABSTRACT Geothermobarometry provides crucial constraints on the physical conditions of metamorphism, offering insights into petrogenetic processes and providing key information on thermal regimes and metamorphic depths to other geological disciplines. However, calibrating a thermobarometer from the natural record is challenging because independent pressure ( P ) and temperature ( T ) estimates are required, and the compositional variation of minerals—governed by multiple metamorphic reactions—must be captured in a complex function. This work calibrates a machine learning thermobarometer for biotite using relative P – T estimates based on mineral assemblage sequences. A neural network is used as a flexible model to fit a high‐dimensional thermobarometric regression curve. To address the challenge of sparse training data, a transfer learning strategy is employed, where the model is primarily trained on a large dataset generated with phase equilibrium modelling before refinement with natural data. A general framework for calibrating machine learning thermobarometers is outlined using a neural network thermobarometer for biotite as an example. Selection of the best‐performing model is guided by k ‐fold cross‐validation alongside complementary accuracy checks using metamorphic sequences and precision assessments via Monte Carlo error propagation. Evaluation on an independent test dataset, compiled from the literature, indicates that the model is a potential biotite single‐crystal thermometer with a root mean square error of ± 45°C, consistent with the estimated uncertainty of Ti‐in‐Bt thermometry applied to the same data. A potential barometer is affected by systematic underestimation of pressures above 0.6 GPa due to regression to the mean of the natural database, which is biased towards low‐pressure metamorphism. This limits its applicability in higher‐pressure regimes. This study highlights the potential of using neural networks with transfer learning in petrological applications since they are often constrained by limited natural data.
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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 | 0.002 | 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".