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Record W2082612276 · doi:10.2166/ws.2009.409

Trihalomethane formation potential in selected drinking waters of Lebanon

2009· article· en· W2082612276 on OpenAlexaff
Lucy Semerjian, John Ojur Dennis, George M. Ayoub

Bibliographic record

VenueWater Science & Technology Water Supply · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTrihalomethaneChlorineChemistryIncubationEnvironmental chemistryChromatographyBiochemistry

Abstract

fetched live from OpenAlex

Laboratory-scale simulated distribution system trihalomethane (SDS-THM) tests were conducted on selected public drinking water sources to predict as well as evaluate trihalomethane formations under controlled laboratory conditions. Varying concentrations of SDS-total trihalomethane at any time (SDS-TTHMT) (2.68–85.78 μg/L) were detected in the two simulated water sources. Evaluating the impact of each test variable on attained SDS-TTHMT levels revealed that for both water sources, the majority of samples exhibited higher SDS-TTHMT levels in the presence of higher chlorine doses. For the two sources, 85 to 87.5 percent of the sample pairs exhibited higher SDS-TTHMT levels at higher incubation temperatures. Incubation periods at which maximum THM levels were attained varied with water source type as well as pH values. All comparable samples exhibited SDS-TTHMT values higher (1.41 and 7.07 folds) than actual surveyed TTHM levels. Statistically, SDST-TTHM showed significant correlations with applied chlorine dose at the 0.05 level, and with TOC, bromides, contact time, and temperature at the 0.01 level. The predictive model, formulated using multiple regression approaches, exhibiting the highest coefficients of determination was logarithmic for the laboratory simulated THM database (r2=0.70; p<0.001) with a very high significance level (<0.01 level).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.195
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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