Bibliographic record
Abstract
This study examined removal of organic matter (OM) by coagulation in drinking water treatment. First, the ability of three coagulants to simultaneously reduce turbidity and remove OM from water was determined. The coagulants tested were the commercial products alum and polyaluminum chloride (PACl) and a polyaluminum hydroxysulfate (PAHS) synthesized in the authors' laboratory. Tannic, humic, lignosulfonic, and salicylic acids (SAL) were used as model organic compounds in cold and warm water. The effect of the concentration of tannic acid was also examined, and semiquantitative relationships between OM concentration and coagulant dosage required were determined. In cold water, PACl and PAHS were efficient coagulants for all organic compounds except SAL (which was not itself removed, although it did not inhibit turbidity reduction). However, alum was a poor coagulant in the presence or absence of OM. In warm water, alum and PACl were the more efficient coagulants. OM had approximately the same effect on alum at both temperatures, but its effect on PACl was somewhat increased in warm water. Although PAHS effectively reduced turbidity in the absence of OM or in the presence of SAL, the presence of the other organic substances greatly increased the coagulant demand in warm water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".