Selecting activated carbon for micropollutant removal in drinking water treatment: Trace capacity number test
Bibliographic record
Abstract
This research explores new GAC testing protocols that can be used to assist in the selection of activated carbon for geosmin and MIB removal in drinking water treatment. The trace capacity number (TCN) in liquid- and gas-phase show promise of being fast and simple tests that yield information about the number of high-energy adsorption sites on carbon: a parameter that is theorized to be more closely indicative of the ability of a particular GAC to adsorb organic micropollutants such as goesmin and MIB. Six different common GACs from several manufacturers were characterized using traditional parameters, as well as the TCN tests. Actual carbon performance was then evaluated in the laboratory using water from Lake Ontario that was spiked with 100 ng/L geosmin and 100 ng/L MIB, and directed through bench-scale column tests. The results indicated that as hypothesized, the conventional GAC characterization parameters (e.g. iodine numbers, adsorption isotherms) did not correlate well to actual geosmin and MIB removal performance as given by the column tests, but that the TCN and column tests yielded a similar best-to-worst ranking of the six carbons.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".