The influence of vegetation on turbulence and flow velocities in European salt‐marshes
Bibliographic record
Abstract
Abstract Flow hindrance by salt‐marsh vegetation is manifested in the structure of the tidal current; it has a significant impact on sediment transport and it has been related to increased sediment accretion. The flow characteristics in three different vegetation types ( Spartina maritima , Sp. anglica and Salicornia sp./ Suaeda maritima ) were measured on three salt‐marshes in Portugal and England. These in situ measurements differ from laboratory flume experiments with ‘clean’ vegetation by the complexity of natural canopies. Skimming flow develops above the Spartina canopy when the vegetation is fully submerged. In this situation, a low turbulence zone with nearly constant velocity in the denser canopy is separated from the skimming flow above by an interface characterized by high Reynolds stresses. In the low turbulence zone, a positive relationship exists between turbulence intensity and shoot density, which is due to wake turbulence generated locally in the canopy. The rate of particle settling should be increased in that zone. The lower limit of skimming flow is best predicted by the height within the canopy that includes 85% of the biomass. For emergent Spartina canopies and the short Salicornia / Suaeda marsh, the maximal velocity‐gradient is shifted upwards compared to a standard boundary layer over bare sediment and the turbulence is attenuated near the bed, but to a lesser extent than for fully submerged Spartina canopies. A turbulence reduction near the bed was observed in all measured profiles; that should enhance sediment deposition and protects the bed against subsequent erosion.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".