Biofilm growth in gravel bed streams controls solute residence time distributions
Bibliographic record
Abstract
Abstract Streambed substrates harbor a rich biome responsible for biogeochemical processing in riverine waters. Beyond their biological role, the presence of benthic and hyporheic biofilms can play an important role in influencing large‐scale transport of solutes, even for conservative tracers. As biofilms grow and accumulate biomass, they actively interact with and influence surface and subsurface flow patterns. To explore this effect, we conducted experiments at the Notre Dame Linked Ecosystems Experimental Facility in four outdoor streams, each with different gravel beds. Over the course of 20 weeks we conducted transport experiments in each of these streams and observed different patterns in breakthrough curves as biofilms grew on the substrate. Biofilms played a major role in shaping the observed conservative transport patterns. Overall, while the presence of biofilms led to a decreased exchange rate between the fast (mobile) and slow (immobile) parts of the flow domain, water that was exchanged tended to be stored in the slow regions for longer times once biofilms had established. More specifically, we observed enhanced longitudinal dispersion in breakthrough curves as well as broader residence time distributions when biofilms were present. Biofilm colonization over time homogenized transport patterns across the four streams that were originally very distinct. These results indicate that stream biofilms exert a strong control on conservative solute transport in streams, a role that to date has not received enough attention.
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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 | 0.000 |
| 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) | 0.001 | 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".