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 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 | 0.001 |
| 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".