Bibliographic record
Abstract
We have investigated the mechanisms involved in dissolved oxygen (DO) transfer from a turbulent flow to an underlying organic sediment bed populated with DO-absorbing bacteria. Our numerical study relies on a previously developed and tested computational tool that couples a bio-geochemical model for the sediment layer and large-eddy simulation for transport on the water side. Simulations have been carried out in an open channel configuration for different Reynolds numbers (Reτ = 180–1000), Schmidt numbers (Sc = 400–1000), and bacterial populations (χ* = 100–700 mg l−1). We show that the average oxygen flux across the sediment-water interface (SWI) changes with Reτ and Sc, in good agreement with classic heat-and-mass-transfer parametrizations. Time correlations at the SWI show that intermittent peaks in the wall-shear stress initiate the mass transfer and modulate its distribution in space and time. The diffusive sublayer acts as a de-noising filter with respect to the overlying turbulence; the instantaneous mass flux is not affected by low-amplitude background fluctuations in the wall-shear stress but, on the other hand, it is receptive to energetic and coherent near-wall transport events, in agreement with the surface renewal theory. The three transport processes involved in DO depletion (turbulent transport, molecular transport across the diffusive sublayer, and absorption in the organic sediment layer) exhibit distinct temporal and spatial scales. The rapidly evolving near-wall high-speed streaks transport patches of fluid to the edge of the diffusive sublayer, leaving slowly regenerating elongated patches of positive DO concentration fluctuations and mass flux at the SWI. The sediment surface retains the signature of the overlying turbulent transport over long time scales, allowed by the slow bacterial absorption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.044 |
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; both teacher heads agree on what is shown here.
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".