Spatiotemporal analysis of bacterial biomass and activity to understand surface and groundwater interactions in a highly dynamic riverbank filtration system
Bibliographic record
Abstract
Characterization of surface water - groundwater interaction in riverbank filtration (RBF) systems is of decisive importance to drinking water utilities due to the increasing microbial and chemical contamination of surface waters. These interactions are commonly assessed by monitoring changes in chemical water quality, but this might not be indicative for microbial contamination. The hydrological dynamics of the infiltrating river can influence these interactions, but seasonal temperature fluctuations and the supply of oxygen and nutrients from the surface water can also play a role. In order to understand the interaction between surface water and groundwater in a highly dynamic RBF system of a large river, bacterial abundance, biomass and carbon production as well as standard chemical parameters were analyzed during a 20 month period under different hydrological conditions. In the investigated RBF system, groundwater table changes exhibited striking dynamics even though flow velocities were rather low under regular discharge conditions. Bacterial abundance, biomass, and bacterial carbon production decreased significantly from the river towards the drinking water abstraction well. The cell size distribution changed from a higher proportion of large cells in the river, towards a higher proportion of small cells in the groundwater. Although biomass and bacterial abundance were correlated to water temperatures and several other chemical parameters in the river, such correlations were not present in the groundwater. In contrast, the dynamics of the bacterial groundwater community was predominantly governed by the hydrogeological dynamics. Especially during flood events, large riverine bacteria infiltrated further into the aquifer compared to average discharge conditions. With such information at hand, drinking water utilities are able to improve their water abstraction strategies and react quicker to changing hydrological conditions in the RBF system.
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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 | 0.001 | 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 teacher head, 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".