Experiments on the effect of hydrograph characteristics on vertical grain sorting in gravel bed rivers
Bibliographic record
Abstract
Desert ephemeral gravel bed streams typically have bed surfaces that are relatively unarmored compared to the substrate below, while gravel bed streams in humid and snowmelt areas typically have well‐armored surfaces. The degree of armoring can be characterized in terms of an armor ratio defined as the ratio of the surface median size to the substrate median size. A set of field data shows desert ephemeral gravel bed streams with armor ratios ranging from 0.5 to 2.4 and with an average value of 1.2. The armor ratio of snowmelt‐fed gravel bed streams in the same set ranges from 2 to 7, with an average value of 3.4. The reason for this difference is sought in terms of differing hydrological characteristics and sediment supply regimes. Thirteen experiments were conducted to study the formation of armoring under a range of hydrological conditions. The experiments have two limiting cases: a relatively flat hydrograph that represents conditions produced by continuous snowmelt and a sharply peaked hydrograph that represents conditions associated with flash floods. All constant hydrograph experiments developed a well‐armored structured surface, while short asymmetrical hydrographs did not result in substantial vertical sorting. All symmetrical hydrographs show some degree of sorting, and the sorting tended to become more pronounced with longer duration. Sediment supply appears to be a first‐order control on bed surface armoring, while the shape of the hydrograph plays a secondary role.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".