Adapting direct filtration to increasing source water dissolved organic carbon using clarification and <scp>granular activated carbon</scp>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Changing source water quality namely through increasing natural organic matter (NOM) concentration challenges surface water treatment, especially direct filtration. We conducted a pilot‐scale assessment of various adaptation strategies (e.g., clarification, granular activated carbon [GAC] filtration) for direct filtration facilities under the stress of rising NOM levels. Recognizing that changing source water can impact broader aspects of treatment, we considered the implications of Fe and Mn removal via KMnO 4 pre‐oxidation. GAC media showed promise as an adaptation strategy, providing ~60% removal of dissolved organic carbon (DOC), and a significant reduction in disinfection by‐product formation potential (DBPfp). However, KMnO 4 pretreatment showed limited Mn and Fe removal, and filters with GAC media released dissolved Mn at up to ~30% of prefilter levels. These data suggest that using GAC may come with the risk of poor Mn removal performance if Mn is not removed during pretreatment. This work highlights the complexities anticipated under emerging climate pressures and emphasizes the need for comprehensive treatment solutions that consider factors beyond NOM.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it