Exploratory study of erosion in cultivated organic soils using cesium-137 measurements
Bibliographic record
Abstract
Concerns about the sustainability of cultivated organic soils in Montérégie (Quebec, Canada), which can also be identified as histosols, led to this exploratory study aimed at quantifying erosion in these soils by using cesium-137 ( 137 Cs) measurements. Soil samples were taken from organic soils in twenty-two fields, and their 137 Cs contents were measured by gamma spectrometry. The estimated mean annual erosion rates (±SD), adjusted with cropping history, ranged from 0.4 ± 6.3 to 14.6 ± 10.7 t/ha. The results obtained are lower than expected, based on information provided by the agricultural producers from whose farms the samples were taken, as well as two studies conducted in the same study area that highlight the importance of cultivated organic soil height loss due to wind erosion. Recommendations are formulated to obtain more precise erosion rates in future research. This study also points out aspects that deserve to be investigated to adapt existing conversion models for erosion assessment using 137 Cs measurements in cultivated organic soils. • Exploratory study of erosion in drained and cultivated organic soils using 137 Cs. • Estimated annual erosion rates, adjusted with cropping history, of 0.4–14.6 t/ha. • Advice for planification of sampling in future study of erosion for these soils. • Need to adapt conversion models for their use in cultivated organic soils.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".