The effect of pH on taste and odor production and control of drinking water
Bibliographic record
Abstract
Abstract There is general agreement that pH is an important parameter in many drinking water treatment and control processes such as taste and odor (T&O) control. However, pH is not usually targeted as a primary control parameter and its effects on T&O are often overlooked in favor of other treatment issues. When it comes to T&O control, treatment alternatives typically focus on oxidation and adsorption processes. Whether within these processes or separately, pH plays an important role and the effects on T&O should be considered. For example, pH plays a role in the speciation of odorous chemicals in the environment, some of which arise in wastewater treatment and others from the occurrence of metals in water. During blooms of algae and cyanobacteria in surface water, pH is an important parameter affecting water quality and T&O. Finally, as pH is important for the sample preservation and analysis of T&O compounds, pH is important in the fate and control of T&O. The objective of this article is to better understand the various ways that pH can influence T&O production, control, and analysis of odorants in water and encourage advancement in the state of the science of pH optimization for T&O control.
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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) | 0.002 | 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".