Gully evolution and geomorphic adjustments of badlands to reforestation
Bibliographic record
Abstract
Abstract Badlands and gullied areas are among those geomorphic environments with the highest erosion rates worldwide. Nevertheless, records of their evolution and their relations with anthropogenic land transformation are scarcer. Here we combine historical data with aerial photographs and tree-ring records to reconstruct the evolution of a badland in a Mediterranean environment of Central Spain. Historical sources suggest an anthropogenic origin of this badland landscape, caused by intense quarrying activities during the 18thcentury. Aerial photographs allowed detection of dramatic geomorphic changes and the evolution of an emerging vegetation cover since the 1960s, due to widespread reforestation. Finally, tree-ring analyses of exposed roots allowed quantification of recent channel incision of the main gully, and sheet erosion processes. Our results suggest that reforestation practices have influenced the initiation of an episode of incision in the main channel in the 1980s, through the hypothesized creation of disequilibrium in water-sediment balance following decoupling of hillslopes from channel processes. These findings imply an asymmetry in the geomorphic response of badlands to erosion such that in the early evolution stages, vegetation removal results in gullying, but that reforestation alone does not necessarily stabilize the landforms and may even promote renewed incision.
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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".