Removal of Taste and Odour Compounds by Conventional Granular Activated Carbon Filtration
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 In recent years, musty-earthy tastes in drinking water have become a seasonal problem (September to November) for communities that draw their water from the upper St. Lawrence River. The source of the problem is attributed to the presence of geosmin and 2-methylisoborneol (MIB) at concentrations ranging from 5 to 60 ng/L. In 1997 and 1998, the Cornwall Water Purification Plant added granular activated carbon (GAC) to conventional gravity filters to control taste and odour compounds. We report on a study to test the efficiency of these conventional GAC-capped filters to reduce geosmin and MIB concentrations in finished water as a function of filter age, initial chlorine residual and contact time. GAC–capped filters removed on average 60% of the MIB and 80% of the geosmin from the source water after 2 and 12 months of filter operation. However, testing after 24 months operation showed reduced efficiency. Amounts removed ranged between 13 and 20% (15% average) for MIB and 50 to 57% (54% average) for geosmin in these filters. Testing also suggested that filter efficiency was greater with longer contact times and with higher chlorine residuals (in the range 0.1 to 0.6 mg free Cl2/L). In addition to carbon age, chlorine residual and contact time, flow channelization and slow release of the taste and odour compound load (particularly MIB) from the GAC bed may affect apparent filter efficiency.
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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 | 0.004 | 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 | 0.000 | 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 it