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; its ability to remove elevated/altered post-fire dissolved organic carbon (DOC) resulting from wildfire ash was therefore investigated. The treatment of low, medium, and high wildfire ash-amended source waters by bench-scale biofilters was evaluated in duplicate. Turbidity and DOC were typically well-removed during periods of stable operation (effluent turbidity ≤ 0.3 NTU in 93% of samples, average DOC removal ~20% in all biofilters during periods of non- impaired DOC removal). Daily DOC removal across all biofilters was generally consistent, suggesting that the wildfire ash and associated water extractable organic matter did not reduce the DOC biodegradation capacity of the biofilters. DOM fractionation indicated that this was because the low molecular weight neutral (which are known to be readily biodegradable) and biopolymer fractions of DOM were reduced; however, humics were largely recalcitrant. Thus, biological filtration may be resilient to wildfire ash-associated DOM threats to drinking water treatment. However, operational resilience may be compromised if the balance between readily removed and recalcitrant fractions of DOM change, as was observed when baseline source water quality fluctuated for brief periods during the investigation.
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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 | 0.001 |
| 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".