The transport of sediment mixtures examined with a birth-deathmodel for grain-size fractions
Bibliographic record
Abstract
Abstract. Bedload transport of sediment mixtures in mountain streams is challenging to predict, with implications for understanding how rivers form and respond to environmental change. Experimental work shows that collective particle entrainment is an important contributing mechanism of bedload transport, but questions remain. We use four different time series of experimental sediment flux for granular particles 4–32 mm in diameter to indirectly examine the role of collective mobilization. Flux was measured at a fixed position in space using an imaging light table. The light table provides a flux measurement that is sampled at a resolution of 1 Hz, and for total time durations ranging from 75 to 240 min. Experimental conditions include periods of statistical steady-state, and transient adjustments due to changes of the upstream supply of water and sediment. We find that despite the contrasting experimental conditions, the time series encode a consistent transport behaviour within the Fourier domain: the transport of finer grain size populations has increasing power density for decreasing frequency, whereas the transport of larger grain size populations has a near constant power density across all frequencies. Hence, smaller particle sizes dominate the power spectra. We seek an explanation for this result, and elaborate on a probabilistic birth-death model introduced to the field by Christophe Ancey and colleagues. Analysis using the expanded birth-death model provides two important results. The transport of smaller particles includes collective entrainment terms that represent grain mobilization due to smaller and larger particle sizes colliding with the streambed surface. In contrast, the transport of larger particles includes collective entrainment terms limited to larger particle sizes. The size-dependent collective controls on particle mobilization is an important finding, and we show that it offers a testable explanation for observed flux differences between smaller and larger particle sizes, common to gravel-bed mountain streams. As a result, our work motivates the need to better understand collective entrainment within the context of granular sediment transport along mountain stream beds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".