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Record W2058572419 · doi:10.1021/jf048665y

Polybrominated Diphenyl Ethers in Retail Fish and Shellfish Samples Purchased from Canadian Markets

2004· article· en· W2058572419 on OpenAlexaffabout
Sheryl A. Tittlemier, Don Forsyth, Kenneth Breakell, Victor Verigin, John J. Ryan, Stephen Hayward

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsHealth Canada
Fundersnot available
KeywordsShellfishShrimpPolybrominated diphenyl ethersFisheryTilapiaMusselTroutWet weightArctic charBiologyEnvironmental chemistryAnimal scienceFish <Actinopterygii>Aquatic animalChemistryPollutantEcology

Abstract

fetched live from OpenAlex

Fish and shellfish retail samples (n = 122) were purchased from three Canadian cities in the winter of 2002 and analyzed for a total of 18 polybrominated diphenyl ether (PBDE) congeners. The samples (salmon, trout, tilapia, Arctic char, mussels, oysters, shrimp, and crab) represented the range of fish and shellfish commercially available to Canadian consumers at the time of purchase. Trout and salmon (geometric mean SigmaPBDE = 1600 and 1500 pg/g, wet weight, respectively) were found to contain significantly higher amounts of PBDEs than the mussel, tilapia, and shrimp groups (geometric mean SigmaPBDE = 260, 180, and 48 pg/g, wet weight, respectively). These differences in SigmaPBDE concentrations among fish and shellfish products were partly driven by differences in lipid content among the samples. Mean SigmaPBDE concentrations in domestic samples were also significantly greater than in imported samples, possibly reflecting global environmental distribution of PBDEs. These concentration differences will contribute to variations in dietary exposure to PBDEs when assorted fish and shellfish items from various origins are consumed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.174
Teacher spread0.168 · 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
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

Citations53
Published2004
Admission routes2
Has abstractyes

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