Influence of biofilms on heavy metal immobilization in sustainable urban drainage systems (SuDS)
Bibliographic record
Abstract
This paper physically and numerically models the influence of biofilms on heavy metal removal in a gravel filter. Experimental flow columns were constructed to determine the removal of Cu, Pb and Zn by gabbro and dolomite gravel lithologies with and without natural biofilm from sustainable urban drainage systems (SuDS). Breakthrough experiments showed that, whilst abiotic gravel filters removed up to 51% of metals, those with biofilms enhanced heavy metal removal by up to a further 29%, with Cu removal illustrating the greatest response to biofilm growth. An advection-diffusion equation successfully modelled metal tracer transport within biofilm columns. This model yielded a permanent loss term (k) for metal tracers of between 0.01 and 1.05, correlating well with measured data from breakthrough experiments. Additional 16S rRNA clone library analysis of the biofilm indicated strong sensitivity of bacterial community composition to the lithology of the filter medium, with gabbro filters displaying Proteobacteria dominance (54%) and dolomite columns showing Cyanobacteria dominance (47%).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".