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Record W1983702067 · doi:10.1002/rcm.1066

Congener‐specific analysis of hexabromocyclododecane by high‐performance liquid chromatography/electrospray tandem mass spectrometry

2003· article· en· W1983702067 on OpenAlexaffabout
Wesley Budakowski, Gregg T. Tomy

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of ManitobaFisheries and Oceans Canada
Fundersnot available
KeywordsChemistryHexabromocyclododecaneChromatographyElectrospray ionizationMass spectrometryTandem mass spectrometryAnalytical Chemistry (journal)ElectrosprayCongenerSelected reaction monitoringDetection limitOrganic chemistry

Abstract

fetched live from OpenAlex

A congener-specific method based on high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC/ES-MS/MS) in the negative ion mode was developed for the analysis of hexabromocyclododecane (HBCDD). On a C(18) analytical column, with a methanol/water mobile phase, the alpha-isomer was completely resolved from the beta- and gamma-isomers while the beta- and gamma-isomers were sufficiently resolved at half their peak heights. The ES spray voltage strongly influenced the intensity of the ion signal. For MS, a source temperature of 500 degrees C and a collision energy of 50 eV were found to be optimum for the [M-H](-) to Br(-) transition. Run-to-run and day-to-day (n = 3) variability was minimal, with relative standard deviations of 2.6-4.1 and 2.4-4.4%, respectively. The limit of detection was 4-6 pg on-column. When applied to tissue samples from Lake Winnipeg fish both alpha- and gamma-isomers of HBCDD were found in low-ng/g (lipid corrected) concentrations.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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 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

Citations102
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueRapid Communications in Mass SpectrometrySame topicToxic Organic Pollutants ImpactFrench-language works237,207