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Record W2800932852 · doi:10.1016/j.dib.2018.05.004

Data on the level of haloacetic acids in indoor swimming pools of Iran: A case study of Tehran

2018· article· en· W2800932852 on OpenAlexfundno aff
Mohammad Hadi Dehghani, Mansoureh Farhang, Ahmad Zarei

Bibliographic record

VenueData in Brief · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsnot available
FundersHospital for Sick Children
KeywordsHaloacetic acidsDichloroacetic acidTrichloroacetic acidChlorineChemistryEnvironmental chemistryTotal organic carbonChromatographyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Haloacetic acids (HAAs) are the second most prevalent class of DBPs after trihalomethanes (THMs) in water disinfected by chlorine compounds. Within this study, we present new data on occurrence and speciation of HAA levels in 15 indoor swimming pools in Tehran in 2017. Five HAAs (HAA5), including monochloroacetic acid, dichloroacetic acid, trichloroacetic acid, monobromoacetic acid, and dibromoacetic acid were analyzed. Levels of pH, total chlorine, and total organic carbon concentration were analyzed as well. Results indicated that the levels of HAA5 in swimming pools in the Tehran ranged from 148 to 3488 µg/L, with an average of 1045.26 µg/L. HAAs in the swimming pools in Tehran might be due to the extensive use of chlorine compounds for disinfection. Therefore, due to the high levels of HAAs, frequent monitoring of HAA levels as well as minimization strategies is needed in these swimming pools.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.214
GPT teacher head0.337
Teacher spread0.123 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

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

Citations18
Published2018
Admission routes1
Has abstractyes

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