Comment on “Pressure‐to‐Depth Conversion Models for Metamorphic Rocks: Derivation and Applications” by Bauville and Yamato
Bibliographic record
Abstract
Abstract Bauville and Yamato (2021, https://doi.org/10.1029/2020gc009280 ) (G‐cubed) propose model‐based methods to convert metamorphic pressures to depths based on the claim that pressure data from global (ultra)high‐pressure([U]HP) rocks challenge the lithostatic assumption and support their model which invokes excessive overpressures. It is argued here that the opposite is true: Natural pressure data are fully consistent with the lithostatic assumption. They reflect selection of (U)HP rocks by accessibility and preservation. The data are however inconsistent with the model predictions of Yamato and Brun (2017, https://doi.org/10.1038/ngeo2852 ) and Bauville and Yamato (2021, https://doi.org/10.1029/2020gc009280 ). Furthermore, their model requires critical assumptions that are not justified by the principles of rock mechanics and unsupported by microstructures from (U)HP rocks.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.021 | 0.042 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".