Meteoric Beryllium‐10 as a Tracer of Erosion Due to Postsettlement Land Use in West‐Central Minnesota, USA
Bibliographic record
Abstract
Abstract Meteoric beryllium‐10 (10Bem, t1/2 = 1.4 Myr) is a cosmogenic radionuclide that remains largely underutilized for deriving hillslope‐scale estimates of erosion on uplands under conditions of land use change. We applied two different models for estimating erosion rates from observed 10Bem concentrations (a one‐dimensional model predicting vertical profiles of 10Bem within hillslope soils [loss only, diffusion only, LODO] and a two‐dimensional model predicting the concurrent evolution of hillslope topography and 10Bem distributions via bioturbation, chemical mobility, and surface erosion [Be2D]). Both models were used to derive pre‐European and post‐European settlement erosion rates (Enat and Epost, respectively) across paired cultivated and uncultivated hillslopes in west‐central Minnesota, USA. Epost estimates from 10Bem were compared to Epost estimates derived from 137Cs inventories and the process‐based Water and Tillage Erosion Model (WaTEM). The results from these models suggest that erosion rates from upper positions on the cultivated hillslope have increased from an average of 0.047 mm/year under natural conditions to Epost values of 3.09 mm/year. The Be2D and LODO models, on average, produced Epost estimates that were similar in magnitude to WaTEM and 137Cs conversion models. This numerical convergence does not imply absolute 10Bem model accuracy, particularly when considering the uncertainties inherent in each approach, but it does suggest that the orders of magnitude increase in estimated erosion rates from Enat to Epost is robust. Additionally, the pattern of Epost estimates produced using 10Bem conversion models is supported by the distribution of soil inorganic carbon at the study site. Our results demonstrate that 10Bem can provide reasonable estimates of both predisturbance and postdisturbance erosion rates in landscapes that have undergone extensive human modification.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".