Spatial assessment of sediment production in a badland catchment using repeat LiDAR surveys, Draix, Alpes de Haute-Provence, France
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Bibliographic record
Abstract
Abstract. With denudation rates locally exceeding one centimetre of weathered marl per year, i.e., more than 200 T ha−1 yr−1, the badlands of the Durance basin in the French Alps are one of the most eroding areas in the world. Since 1983, the Draix-Bléone Observatory has been using hydro-sedimentary stations to monitor several of these small, unmanaged badland catchments, where the hydrological response to seasonal storms is rapid and intense. In order to fingerprint soil loss in the 86 ha Laval basin, we combine outlet records with an analysis of airborne and UAV LiDAR data taken over a 6-year period, alongside a bulk density model to account for porosity variations with depth and drainage network reconstruction. This allows us to map mass movements and determine a sediment budget at catchment scale. We find that landslides and crest failures represent very active areas, accounting for at least 15 % of the watershed's sediment budget throughout the period under study, despite affecting only 1 % of the bare surfaces. They contribute to the high erosion rates observed in low-drainage areas, with up to two centimetres of fresh marl lost per year, 3.5 times the average value on the rest of the bare slopes. Despite certain methodological constraints, our approach seems very promising at identifying local erosion hotspots, quantifying their contribution to the sediment budget and assessing sediment transport across geomorphological units. It could also be adapted to time series and more detailed identification of geomorphic processes in order to monitor the dynamics of badland catchments in a changing climate.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it