Muon paleotopometry
Bibliographic record
Abstract
Abstract. Recent advances in measuring muon fluxes at great depths for neutrino experiments, along with improvements in in-situ terrestrial cosmogenic nuclide (TCN) measurements, have enhanced our understanding of muogenic nuclide production rates at decametre and hectometre depths. These developments allow us to explore muogenic TCN as a tool for estimating long-term (>10⁵ years) erosion rates. There are theoretical advantages of utilizing muogenic TCN for long-term landscape evolution analysis (µ-paleotopometry). We summarize recent advances in knowledge of deep muon flux used in the interpretation of neutrino interactions at kilometre depths. We discuss strategies being considered for the µ-paleotopometry method to address otherwise intractable landscape evolution questions. We demonstrate an achievable resolution of calculated erosion rates with TCN measurements in quartz at rock depths of 37.8 x 103 to 67.5 x 103 g cm-2 (~ 140 and 250 m). In settings where assumptions of constant erosion rate are suitable, the uncertainty is controlled by nuclide concentration measurement error and the effective time (duration over which the isotope concentration reflects the erosion history) is limited by radio-decay. Other environmental complexities, such as variable glacier cover, unknown complexity in initial topography, or mineral composition of rock may restrict the addressable questions and limit precision. Where a time-varying erosion rate is sought, deeper-time variances have inferior representation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".