Evaluating young fluvial terrace riser degradation using a nonlinear transport model: Application to the Kongur Normal Fault in the Pamir, northwest China
Bibliographic record
Abstract
Abstract Selecting an appropriate hillslope transport model and calibrating model parameters are essential for morphological dating of fault and fluvial scarps. In this paper, we refine the method of profile‐based morphologic dating by updating the representation of nonlinearity in sediment flux dependence on the hillslope gradient. We apply this revised method to fluvial scarps bounding fluvial terraces offset along the Kongur Normal Fault in the semi‐arid high‐altitude Pamir mountains, northwestern China. One of these terraces, the T3 surface, is dated to 7.0 +1.9 / −1.6 kyr using 10 Be cosmogenic depth profile analysis. Well‐preserved, dated terraces make this an ideal site to test the utility of morphological modelling in constraining ages of the young terrace risers. To do this, 35 topographic swath profiles across the terrace risers are extracted from a 0.2 m‐resolution digital elevation model produced using structure from motion from photos collected by an unmanned aerial vehicle. The best estimates of morphological age are 13.9 ± 1.3 m 2 for the riser T3/T4 and 11.9 ± 1.3 m 2 for T2/T3 using a linear diffusion approach. These two morphological ages overlap within uncertainty and fail to distinguish between two young terrace risers. Alternatively, we employed a nonlinear diffusion model, calibrated with transport constant k = 1 m 2 kyr −1 , nonlinearity n = 2, and critical gradient S c = tan(33°). This nonlinear model produces ages of 7.3 ± 0.5 kyr for T3/T4 and 4.0 ± 0.2 kyr for T2/T3; these ages are consistent with terrace surface ages deduced by using vertical offset divided by independently determining average throw rate. This comparison shows the advantage of a nonlinear model in defining ages of young scarps. Furthermore, we explored the minor effect of heterogeneous degradation along steep sections of the scarp profiles. The nonlinear scarp modelling scheme we develop in this paper is suitable for studying scarp degradation in other regions. © 2020 John Wiley & Sons, Ltd.
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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 | 0.001 | 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".