A geologically constrained Monte Carlo approach to modeling exposure ages from profiles of cosmogenic nuclides: An example from Lees Ferry, Arizona
Bibliographic record
Abstract
We present a user‐friendly and versatile Monte Carlo simulator for modeling profiles of in situ terrestrial cosmogenic nuclides (TCNs). Our program (available online at http://geochronology.earthsciences.dal.ca/downloads‐models.html ) permits the incorporation of site‐specific geologic knowledge to calculate most probable values for exposure age, erosion rate, and inherited nuclide concentration while providing a rigorous treatment of their uncertainties. The simulator is demonstrated with 10 Be data from a fluvial terrace at Lees Ferry, Arizona. Interpreted constraints on erosion, based on local soil properties and terrace morphology, yield a most probable exposure age and inheritance of 83.9 −14.1 +19.1 ka, and 9.49 −2.52 +1.21 × 10 4 atoms g −1 , respectively (2 σ ). Without the ability to apply some constraint to either erosion rate or age, shallow depth profiles of any cosmogenic nuclide (except for nuclides produced via thermal and epithermal neutron capture, e.g., 36 Cl) cannot be optimized to resolve either parameter. Contrasting simulations of 10 Be data from both sand‐ and pebble‐sized clasts within the same deposit indicate grain size can significantly affect the ability to model ages with TCN depth profiles and, when possible, sand—not pebbles—should be used for depth profile exposure dating.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".