Selecting and Applying Cesium‐137 Conversion Models to Estimate Soil Erosion Rates in Cultivated Fields
Bibliographic record
Abstract
The fallout radionuclide cesium-137 ((137)Cs) has been successfully used in soil erosion studies worldwide. However, discrepancies often exist between the erosion rates estimated using various conversion models. As a result, there is often confusion in the use of the various models and in the interpretation of the data. Therefore, the objective of this study was to test the structural and parametrical uncertainties associated with four conversion models typically used in cultivated agricultural landscapes. For the structural uncertainties, the Soil Constituent Redistribution by Erosion Model (SCREM) was developed and used to simulate the redistribution of fallout (137)Cs due to tillage and water erosion along a simple two-dimensional (horizontal and vertical) transect. The SCREM-predicted (137)Cs inventories were then imported into the conversion models to estimate the erosion rates. The structural uncertainties of the conversion models were assessed based on the comparisons between the conversion-model-estimated erosion rates and the erosion rates determined or used in the SCREM. For the parametrical uncertainties, test runs were conducted by varying the values of the parameters used in the model, and the parametrical uncertainties were assessed based on the responsive changes of the estimated erosion rates. Our results suggest that: (i) the performance/accuracy of the conversion models was largely dependent on the relative contributions of water vs. tillage erosion; and (ii) the estimated erosion rates were highly sensitive to the input values of the reference (137)Cs level, particle size correction factors and tillage depth. Guidelines were proposed to aid researchers in selecting and applying the conversion models under various situations common to agricultural landscapes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".