Erosion controls vegetation recolonization in Draix-Bleone badlands
Bibliographic record
Abstract
Vegetation and erosion interact with each other through a variety of processes, contributing to the formation and evolution of landscapes. The present work focuses on the humid badlands of Draix-Bleone observatory, in the French Alps. In this observatory, long-term records of hydrology and sediment fluxes are available for several catchments of varying size and vegetation cover. We aim to characterize and quantify the interactions between vegetation and erosion in these badlands. One the one hand, we previously found that vegetation, where it is able to maintain, strongly limits badland erosion. On the other hand, vegetation recolonization has been observed over the last decades and we hypothesize that this growth is controlled by topographic and erosive mechanisms. We use aerial images for several dates in the Laval catchment of size 0.86 km2. We classify each image to map vegetation cover and compare the extent of vegetation cover from one date to the other. We then extract the newly vegetated areas and search for environmental factors that can explain why these areas have been colonized rather than others. We combine factors such as slope and drainage area that are related to erosive processes, to biological factors that relate to the dispersion and colonization capacity of previously existing vegetation. Preliminary findings indicate that vegetation has mainly recolonized areas that are in the vicinity of existing vegetation patches and with low to intermediate slopes. No effect of aspect is found. This suggests that recolonization is limited by erosive processes, but not by water availability.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".