Sand, Gravel, Cobbles, and Boulders: Detrital Thermochronology Shows that One Size Does Not Tell All
Bibliographic record
Abstract
Abstract Detrital thermochronology has been used to measure sediment source elevations, and thus to quantify spatial variations in sediment production and erosion in steep mountain catchments. Samples commonly include a small fraction of the sediment sizes present on mountain streambeds, which according to previous modeling may not adequately represent sediment production where hillslope sediment sizes vary or where sediment breaks down during transport. Here we explore what can be learned from multiple sizes by quantifying source elevation distributions for 12 sediment size classes collected from Inyo Creek in the eastern Sierra Nevada, California. To interpret these data, we use a new analytical framework that identifies both the elevations where sediment sources deviate from catchment hypsometry and the likelihood that observed cumulative deviations could occur by chance. We find that sediment in four gravel and cobble size classes originates preferentially from higher elevations, either because erosion rates are faster or because these sizes are disproportionately represented in the sediment from high elevations. Conversely, boulders in the stream originate mostly from low elevations near the sample point, possibly reflecting the breakdown of boulders from high elevations during transport. While source elevations of finer sediment sizes are statistically indistinguishable from hypsometry, we show that these sizes are unlikely to be consistent with uniform sediment production because they cannot be considered in isolation from the coarser sizes. Our source elevation distributions from sand, gravel, cobbles, and boulders show that no one size can tell the rich story of sediment production and evolution, and highlight opportunities for future work.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".