Biological Filtration is Resilient to Wildfire Ash-Associated Organic Carbon Threats to Drinking Water Treatment
Bibliographic record
Abstract
Elevated/altered levels of dissolved organic matter (DOM) in water can be challenging to treat after wildfire. Biologically mediated treatment removes some DOM; here, its ability to remove elevated/altered postfire dissolved organic carbon (DOC) resulting from wildfire ash was investigated for the first time. Treatment of wildfire ash-amended (low, moderate, high) source waters by bench-scale biofilters was evaluated in duplicate. Turbidity and DOC were typically well-removed (effluent turbidity ≤0.3 NTU; average DOC removal ∼20%) in all biofilters during periods of stable source water quality. Daily DOC removal across all biofilters (ash-amended and controls) was generally consistent, suggesting that (i) the biofilter DOC biodegradation capacity was not deleteriously impacted by the ash and (ii) the biofilters buffered the ash-associated increases in water extractable organic matter. DOM fractionation indicates this was because the biodegradable low molecular weight neutral fractions of DOM, which increased with ash addition, were reduced by biofiltration while humic substances were largely recalcitrant. Thus, biological filtration was resilient to wildfire ash-associated DOM threats to drinking water treatment, but operational resilience may be compromised if the balance between readily removed and recalcitrant fractions of DOM change, as was observed during brief periods herein.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".