Mapping of erosion rates in marly badlands based on a coupling of anatomical changes in exposed roots with slope maps derived from LiDAR data
Bibliographic record
Abstract
Black marls form very extensive outcrops in the Alps and constitute some of the most eroded terrains, thus causing major problems of sedimentation in artificial storage systems (e.g. reservoirs) and river systems. In the experimental catchments near Draix (France), soil erosion rates have been measured in the past at the plot scale through a detailed monitoring of surface elevation changes and at the catchment scale through continuous monitoring of sediment yield in traps at basin outlets. More recently, erosion rates have been determined by means of dendrogeomorphic techniques in three monitored catchments of the Draix basin. A total of 48 exposed roots of Scots pine have been sampled and anatomical variations in annual growth rings resulting from denudation analysed. At the plot scale, average medium‐term soil erosion rates derived from exposed roots vary between 1·8 and 13·8 mm yr−1 (average: 5·9 mm yr−1) and values are significantly correlated with slope angle. The dendrogeomorphic record of point‐scale soil erosion rates matches very well with soil erosion rates measured in the Draix basins. Based on the point‐scale measurements and dendrogeomorphic results obtained at the point scale, a linear regression model involving slope angle was derived and coupled to high‐resolution slope maps obtained from a LiDAR‐generated digital elevation model so as to generate high‐resolution soil erosion maps. The resulting regression model is statistically significant and average soil erosion rates obtained from the areal erosion map (5·8, 5·2 and 6·2 mm yr−1 for the Roubine, Moulin and Laval catchments, respectively) prove to be well in concert with average annual erosion rates measured in traps at the outlet of these catchments since 1985 (6·3, 4·1 and 6·4 mm yr−1). This contribution demonstrates that dendrogeomorphic analyses of roots clearly have significant potential and that they are a powerful tool for the quantification and mapping of soil erosion rates in areas where measurements of past erosion is lacking.
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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 | 0.001 |
| 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) | 0.000 | 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".