Channel mobility drives a diverse stratigraphic architecture in the dryland Mojave River (California, USA)
Bibliographic record
Abstract
Abstract The links between flood frequency and rates of channel migration are poorly defined in the ephemeral rivers typical of arid regions. Exploring these links in desert fluvial landscapes would augment our understanding of watershed biogeochemistry and river morphogenesis on early Earth (i.e. prior to the greening of landmasses). Accordingly, we analyse the Mojave River (California), one of the largest watercourses in the Great Basin of the western United States. We integrate discharge records with channel‐migration rates derived from dynamic time‐warping analysis and chronologically calibrated subsidence rates, thereby constraining the river's formative conditions. Our results reveal a slight downstream decrease in bankfull discharge on the Mojave River, rather than the downstream increase typically exhibited by perennial streams. Yet, the number of days per year during which the channel experiences bankfull or higher stages is roughly maintained along the river's length. Analysis of historical peak flood records suggests that the incidence of channel‐formative events responds to modulation in watershed runoff due to the precipitation in the river's headwaters over decades to centuries. Our integrated analysis finally suggests that, while maintaining hydraulic geometries that are fully comparable with many other rivers worldwide, ephemeral desert rivers akin to the Mojave are capable of generating a surprisingly wide range of depositional geometries in the stratigraphic record. © 2020 John Wiley & Sons, Ltd.
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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".