Bibliographic record
Abstract
Abstract. With denudation rates locally exceeding one centimetre of fresh marl per year, i.e., more than 250 T.ha−1.yr−1, the badlands of the Durance basin in the French Alps makes it one of the world’s most heavily eroding areas. Since 1983, the Draix-Bléone Observatory has been using hydro-sedimentary stations to instrument several of these small, unmanaged badland catchments, where the hydrological response to seasonal storms is rapid and intense. We combine such chronicles at the outlet of the Laval basin (86 ha) with a six-year diachronic analysis of airborne and UAV LiDAR data and a bulk density modelling to map mass movements and constrain a catchment-scale mass balance. We find out that landslides and crests failures represents very active areas, accounting for at least 15 % of the sediment budget of the watershed, while affecting only 1 % of the bare surfaces. They contribute to making the low drainage areas the places with highest erosion rates, reaching as much as two centimetres of fresh marl per year, 3.5 times more than the average value on denuded slopes. Despite some methodological constraints, our approach seams very promising at quantifying and localising the erosion hotspots as well as assessing sediment transport through critical zone compartments, and could be adapted to time series for monitoring the dynamics of badland catchments in a changing climate.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.187 | 0.156 |
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".