Abstract: Pleistocene landscape evolution of the southern Central Andes quantified with cosmogenic nuclide techniques
Bibliographic record
Abstract
Jose Luis Antinao1, John Gosse1, Marc Caffee2, and Robert Finkel3 1. Department of Earth Sciences, Dalhousie University, Halifax, NS B3H 4J1 Canada ¶ 2. Physics Department, Purdue University, West Lafayette, IN 47097, USA ¶ 3. Center of Accelerator Mass Spectrometry, Lawrence Livermore National Lab, Lawrence, CA, 94550 USA Landscape evolution studies depend critically on the quantification of long-term denudation rates. These are difficult to obtain in active mountain belts, because sediments are normally rapidly eroded in these environments. Terrestrial in situ cosmogenic nuclides (10Be and 36Cl) have been used in this study in different ways to estimate denudation rates in the southern Central Andes of Chile. An inventory of large bedrock involved landslides, with a chronology supported by 10Be and 36Cl exposure dating provides reconstructed sediment volumes to estimate denudation rates from landslides during the Pleistocene. Simultaneously, 36Cl basin-wide average erosion rates were obtained for small catchments inside the same area. Both long-term (103–106 a) estimates were compared to short-term estimates based on suspended sediment records for the last 30 years. Rates of denudation of ~0.1 mm/a were obtained using the landslide inventory data, similar to the 36Cl basin-wide average erosion rates (0.15–0.23 mm/a). The estimations from suspended sediment records for the last 30 years show variable values, depending on their position along the orogen, between 0.03 to 0.15 mm/a. As accumulation inside the range is minor, there are two possibilities that can explain these observations, setting aside scale differences for the studied areas. Although for one area all estimates are similar within uncertainty, for others present day sediment transport by large rivers is out of equilibrium with long-term transport. The system might be currently transport-limited but during the Pleistocene it must have had periods of increased sediment discharge. A second alternative is that the bedload component of sediment transport needs to be incorporated more precisely into the estimations from suspended sediment records. Application of three bedload transport theoretical formulations to major rivers of the region supports this asseveration, suggesting that in this environment bedload can represent up to 80% of the sediment transport.
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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".