Magnitude and timing of transient incision resulting from large‐scale drainage capture, Sutlej River, Northwest Himalaya
Bibliographic record
Abstract
Abstract Few studies have constrained the magnitudes and timescales associated with large‐scale drainage captures (areas >10 3 km 2 ), even though these constraints are crucial to reconstruct sediment budgets, assess the potential for drainage reorganization to be preserved in the rock record, and determine the extent to which environmental signals (i.e., structures, composition and fossil assemblages within sedimentary rocks that are influenced by sediment supply and transport) are representative of conditions during deposition. In this work, we characterize the Pleistocene capture of the Zhada Basin, an ~23 000 km 2 extensional basin in southern Tibet, by the Sutlej River, a prominent tributary to the Indus River. We quantify the magnitudes and timescales of capture‐driven erosion using knickpoint celerity modelling, paleotopographic reconstructions, 10 Be‐derived denudation rates, and topographic analyses of drainage divides. We find that capture has removed 2010 ± 400 km 3 of sediment from the Zhada Basin, increasing sediment supply to the Sutlej network by 17%–29% since 735 ± 269 ka. This work represents a crucial step towards reconstructing the Pleistocene sediment budget of the Indus sedimentary system and identifying potential impacts from sediment redistribution. We also identify several plausible tectonic or autogenic mechanisms that may have facilitated capture of the Zhada Basin, including: (1) preferential erosion of weak lithologies along active faults, (2) headward erosion in response to prior capture of the Spiti River and (3) headward erosion generated by breaching of a structural culmination downstream (the Kullu‐Rampur Window). This provides a framework to assess the mechanistic links between arc‐parallel extension, large‐scale drainage capture, landscape evolution and orogenic wedge deformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".