Bibliographic record
Abstract
Reactions between natural organic matter (NOM) and chlorine during disinfecting drinking water form trihalomethanes (THMs) and other chlorinated by products (CBPs), some of which are possible carcinogen to human health. A number of models have been developed to predict THMs formation since the discovery of THMs in drinking waters. A fraction of these models used a total of 22 different parameters while individual models used 3 to 8 parameters. Some existing models incorporated more than one parameter from total organic carbon (TOC), dissolved organic carbon (DOC) and UV absorption capacity at 254 nm (UV254), while all of these three characterize NOM in water; thus, there exist a possibility of illconditioned coefficient estimation. This paper presents the results of an experimental investigation on different parameters from four water supply systems in Newfoundland, Canada. Strong correlations were found among total organic carbon (TOC), dissolved organic carbon (DOC) and UV absorption capacity at 254 nm (UV254). This study along with the past studies identified pH, temperature and reaction time as significant for THMs formation; however, some existing models ignored these parameters. Although these models have good performance in predicting THMs formation in respective environmental conditions, some models might suffer weakness from mathematical point of view; thus needs to be carefully applied. This study recommends using one parameter from TOC, DOC and UV254 and chlorine dose, pH, temperature and reaction time for future modeling.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".