LiDAR assessment of sediment transport in a small, highly erosive alpine catchment
Bibliographic record
Abstract
With rates locally exceeding one centimeter of denudation per year [1,2], i.e., more than 100 t/ha/year, the Durance basin in the French Alps is one of the world’s most heavily eroding areas [3]. A combination of favorable conditions explains this phenomenon, including a very steep topography, sparse vegetation and a particular susceptibility of the Jurassic black marls, also called Terres Noires, to seasonal climatic forcing [4]. The Draix-Bléone observatory uses hydro-sedimentary stations to instrument several of these small, non-anthropized catchments, where hydrological responses to seasonal storms are rapid and intense [5]. The work presented combines the use of LiDAR time series from airborne, UAV and ground measurements, with sediment flux chronicles recorded at the outlet of the Laval catchment, a small steep watershed (0.86 m2), to address questions of upstream/downstream transport modalities. An assessment of the potential of remote sensing methods for attributing the contributions of each critical zone compartment (channel, gullies, landslides, etc.) to the erosive dynamics of this basin and its connectivity is carried out.References :[1] N. Mathys, S. Brochot, M. Meunier and D. Richard (2003). Erosion quantification in the small marly experimental catchments of Draix (Alpes de Haute Provence, France). Calibration of the ETC rainfall-runoff-erosion model. CATENA. 50(2–4):527–548. DOI : 10.1016/S0341-8162(02)00122-4.[2] A. Carriere, C. Le Bouteiller, G. E. Tucker, S. Klotz, and M. Naaim (2020). Impact of vegetation on erosion: Insights from the calibration and test of a landscape evolution model in alpine badland catchments. Earth Surf. Process. Landf., 45(5):1085–1099. DOI : 10.1002/esp.4741.[3] D. E. Walling (1998). Measuring sediment yield from river basins. in Soil Erosion Research Methods (R. Lal, Ed.). Soil and Water Conservation Society, Iowa, USA, pp 39–73.[4] L. Descroix and N. Mathys (2003). Processes, spatio-temporal factors and measurements of current erosion in the French Southern Alps: A review. Earth Surf. Process. Landf. 28( 9): 993–1011. DOI : 10.1002/esp.514.[5] Draix-Bleone Observatory. (2015). Observatoire hydrosédimentaire de montagne Draix-Bléone [Data set]. Irstea. DOI : 10.17180/obs.draix
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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 | 0.000 | 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) | 0.001 | 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".