Conceptual proof, current application, and lab procedures to quantify crustal thickness variations with muon paleotopometry
Bibliographic record
Abstract
Measuring the topographic relief evolution over hundreds of thousands to million-year timescales remains challenging. Current approaches use a mix of basin stratigraphy, numerical modelling of terrestrial cosmogenic nuclide (TCN) exposure ages on strath terraces, and exhumation histories based on thermochronology or drainage basin evolution. Yet, even a combined mix of these methods is incapable of quantifying the rate changes with precisions needed to differentiate climate from tectonic drivers over multiple glacial cycles and longer timescales.The recently conceived muon-paleotopometry (MPT) approach is tailored to close the methodological gap of determining relief generation. MPT exploits the dependence of cosmic ray muon flux on crustal shielding depth. The spatial concentration pattern of multiple muon-induced TCN measured along a near-horizontal transect under valleys and peaks relates directly to the history of changes (positive or negative) in crustal thickness. MPT allows paleotopometry measurements above the sample datum over an isotope-specific monitoring duration. By sampling at depths of hectametres, long-lived TCN (e.g., 10Be, 26Al) are not sensitive to minor short-term (150 m) and update on the progress for the current large-scale relief investigation of the European Alps.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.029 |
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".