Substrate size and heterogeneity control anomalous transport in small streams
Bibliographic record
Abstract
Abstract In alluvial systems, substrate characteristics play a critical role in slowing downstream transport of both water and solutes. We present results from solute injection experiments testing the influence of sediment size (pea gravel versus coarse gravel) and heterogeneity (alternating sections versus well‐mixed reaches) on solute transport dynamics in four experimental streams at the Notre Dame Linked Experimental Ecosystem Facility. The stream with pea gravel resulted in more long‐term retention than the stream with coarse gravel, whereas both streams with heterogeneous substrate (alternated and mixed) fell between with similar late‐time scaling. Inverse modeling of solute breakthrough curves suggested that residence times were distributed according to a truncated power law. While conservative solute transport in all four streams was anomalous, truncation times were influenced by sediment size, with the smaller pea gravel exhibiting a later truncation time than the coarse gravel, and the two streams with heterogeneous substrate having an intermediate cutoff. These results uniquely associate transport scaling with substrate characteristics in fluvial systems, revealing truncation time scales that had been previously predicted but not observed and quantified in field conditions. Because both benthic (i.e., substrate‐water interface) and subsurface hyporheic regions are known biogeochemical hot spots, relating physical characteristics to the macroscopic transport behavior could be crucial to improve our estimates of solute export from fluvial systems.
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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 | 0.000 | 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) | 0.000 | 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".