Erosional Response to Pleistocene Climate Changes in the Brazilian Highlands
Bibliographic record
Abstract
Abstract Plio‐Quaternary climatic changes are considered to be a key driver of landscape evolution, but many unresolved questions remain, such as the extent of the impact of major climatic shifts such as the Mid‐Pleistocene Transition (MPT). Various geochronological methods are available to infer changes in surface processes over the Plio‐Quaternary, and Terrestrial Cosmogenic Nuclides (TCN) have proven to be one of the most efficient tools to reconstruct paleo‐denudation. Implementing these approaches requires very specific conditions, such as well‐preserved and extensive sediment sequences. Developing alternative methods to document the evolution of denudation is thus of major interest to retrieve information on the evolution of denudation in places where recent detrital sediment records are absent. We explore the evolution of landscape erosion over a 1 Ma timescale in an intra‐cratonic setting, the Espinhaço mountain range (Brazil), with a new data set of detrital cosmogenic nuclide concentrations (26Al–10Be). We observe a systematic disequilibrium in the 26Al/10Be ratio, which we interpret as resulting from the combination of soil mixing and a significant increase in the intensity of surface processes, close to the MPT. We discuss the different scenarios with respect to available local and global data concerning the relationships between climate evolution and erosion over this time period. Our results have important implications for the interpretation of the denudation rates derived from TCN concentrations under steady states assumption, in landscapes with low erosion rates, which have a long memory for surface processes history.
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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 | 0.001 |
| 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".