Incision History of the Three Gorges, Yangtze River Constrained From Inversion of River Profiles and Low‐Temperature Thermochronological Data
Bibliographic record
Abstract
Abstract We reconstruct the incision history of bedrock rivers based on inverse modeling of the long profile of a river channel and the low‐temperature thermochronological data. Our approach first infers an erodibility‐dependent incision history through a linear inversion of the channel elevations of the river. Then to calibrate the reconstructed incision history in the geological timescale, we constrain the erosional efficiency by optimizing the erosion process of the river catchment using a Bayesian analysis, such that the exhumation and cooling paths of bedrocks in the catchment conform to the observed thermochronological ages. We apply this approach to estimate the incision history of the Three Gorges, Yangtze River in East Asia. We modeled the incision histories of three tributaries on the mainstem Yangtze River near the eastern end of the Three Gorges area, assuming that the gorge incision was driven by increased upstream drainage area of the Upper Yangtze (Scenario 1) or local tectonic uplift (Scenario 2). The results of both scenarios suggest an early Miocene onset of the incision of Three Gorges, that is, 18 ± 6 Ma or 21 ± 4 Ma, respectively. During the Pliocene, our models suggest a significant decrease in the gorge incision rate. By comparing the estimated gorge incision history to the late Cenozoic denudation of the eastern Tibetan Plateau and the regional climate change, we suggest that the incision of the Three Gorges has been heavily affected by the development of the Upper Yangtze River and the East Asian monsoon.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".