Influence of particle lithology, size and angularity on rates and products of bedload wear: An experimental study
Bibliographic record
Abstract
Abstract Physical wear during bedload transport influences downstream changes in the size distributions and shapes of riverbed sediment, which in turn affect myriad fluvial processes ranging from incision into bedrock to provision of aquatic habitat. Here we use laboratory tumbling experiments to address several remaining knowledge gaps, including the roles of lithologic susceptibility to fragmentation, particle size distributions and particle angularity in controlling wear rates and the size distribution of wear products. To focus on the dynamics of particle wear in headwater channels, we used initially angular sediment and ran the experiments until 10% of the initial bedload particle mass had been lost to fine‐grained wear products. We measured individual particle mass and diameter by hand and used photo analysis to quantify particle shape and angularity. We find strikingly different wear patterns between limestone and welded tuff. Although wear rates in the tuff were an order of magnitude slower than the limestone, tuff wear was primarily by fragmentation while limestone wear was dominantly by attrition. Fragmentation of the tuff resulted in a widening of the bedload size distribution, a lack of consistent particle rounding and a fine‐wear product dominated by sand. In contrast, limestone particles rounded substantially and produced silt‐sized wear products. Differences in wear product size and susceptibility to fragmentation may reflect contrasts in the size distribution of mineral crystals in each lithology. Wear rates in both rock types declined substantially with increasing cumulative mass loss, due to rounding in the limestone and reduced fragmentation in the tuff. These results suggest that applications of the conventional exponential model to predict mass loss with travel distance need to account for lithologic influence on susceptibility to fragmentation and the influence of rounding on particle wear rates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".