Comment on “Volcanic monitoring of the 2021 La Palma eruption using long-period magnetotelluric data”
Bibliographic record
Abstract
Tracking changes in electrical resistivity structure at volcanoes as a monitoring method using magnetotelluric (MT) data is an important goal which has been shown to have significant benefits for understanding magmatic systems 1 , 2 , 3 , 4 , 5 . However, as with any developing method, care must be taken to ensure robust results. We believe that the recent study by Ref. 6 (herein referred to as PV23) lacks sufficient evidence to support the claim that observed temporal variations in MT data are due to active processes within the crustal magmatic system. Below, we argue that the temporal variations shown in PV23 are not due to deep magmatic processes related to the Dec. 2021 eruption on La Palma and are instead due to problems with the geoelectric field data quality likely related to electrode instability along a single dipole. The fact that the observed temporal variations are not due to deep magmatic processes significantly undermines the interpretation and conclusions of PV23.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.049 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 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".