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Record W2598828865 · doi:10.1016/j.yrtph.2017.03.022

Development of biomonitoring equivalents for barium in urine and plasma for interpreting human biomonitoring data

2017· article· en· W2598828865 on OpenAlexafffund
Devika Poddalgoda, Kristin Macey, Henry Assad, Kannan Krishnan

Bibliographic record

VenueRegulatory Toxicology and Pharmacology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsHealth Canada
FundersHealth CanadaU.S. Environmental Protection Agency
KeywordsBiomonitoringBariumPopulationUrineChemistryEnvironmental chemistryMedicineBiochemistryEnvironmental health

Abstract

fetched live from OpenAlex

The objectives of the present work were: (1) to assemble population-level biomonitoring data to identify the concentrations of urinary and plasma barium across the general population; and (2) to derive biomonitoring equivalents (BEs) for barium in urine and plasma in order to facilitate the interpretation of barium concentrations in the biological matrices. In population level biomonitoring studies, barium has been measured in urine in the U.S. (NHANES study), but no such data on plasma barium levels were identified. The BE values for plasma and urine were derived from U.S. EPA's reference dose (RfD) of 0.2 mg/kg bw/d, based on a lower confidence limit on the benchmark dose (BMDL05) of 63 mg/kg bw/d. The plasma BE (9 μg Ba/L) was derived by regression analysis of the near-steady-state plasma concentrations associated with the administered doses in animals exposed to barium chloride dihydrate in drinking water for 2-years in a NTP study. Using a human urinary excretion fraction of 0.023, a BE for urinary barium (0.19 mg/L or 0.25 mg/g creatinine) was derived for US EPA's RfD. The median and the 95th percentile barium urine concentrations of the general population in U.S. are below the BE determined in this study, indicating that the population exposure to inorganic barium is expected to be below the exposure guidance value of 0.2 mg/kg bw/d.

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 score0.052
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.076
GPT teacher head0.385
Teacher spread0.309 · 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

Citations49
Published2017
Admission routes2
Has abstractyes

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