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Record W1977194577 · doi:10.1002/ieam.216

Use of trophic magnification factors and related measures to characterize bioaccumulation potential of chemicals

2011· article· en· W1977194577 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsEnvironment and Climate Change CanadaSimon Fraser University
Fundersnot available
KeywordsBiomagnificationBioaccumulationTrophic levelBioconcentrationEnvironmental scienceFood webEnvironmental chemistryBiochemical engineeringEcologyBiologyChemistryEngineering

Abstract

fetched live from OpenAlex

Recent technical workgroups have concluded that trophic magnification factors (TMFs) are useful in characterizing the bioaccumulation potential of a chemical, because TMFs provide a holistic measure of biomagnification in food webs. The objectives of this article are to provide a critical analysis of the application of TMFs for regulatory screening for bioaccumulation potential, and to discuss alternative methods for supplementing TMFs and assessing biomagnification in cases where insufficient data are available to determine TMFs. The general scientific consensus is that chemicals are considered bioaccumulative if they exhibit a TMF > 1. However, comparison of study-derived TMF estimates to this threshold value should be based on statistical analyses such that variability is quantified and false positive and false negative errors in classification of bioaccumulation potential are minimized. An example regulatory decision-making framework is presented to illustrate the use of statistical power analyses to minimize assessment errors. Suggestions for considering TMF study designs and TMFs obtained from multiple studies are also provided. Alternative bioaccumulation metrics are reviewed for augmenting TMFs and for substituting in situations in which field data for deriving TMFs are unavailable. Field-derived, trophic level-normalized biomagnification factors (BMF(TL) s), biota-sediment accumulation factors (BSAF(TL) s), and bioaccumulation factors (BAF(TL) s) are recommended if data are available, because these measures are most closely related to the biomagnification processes characterized by TMFs. Field- and laboratory-derived BAFs and bioconcentration factors are generally less accurate in predicting biomagnification. However, bioconcentration factors and BAFs remain useful for characterizing bioaccumulation as a result of the transfer of chemicals from abiotic environmental compartments to lower trophic levels. Modeling that incorporates available laboratory and field data should also be considered for augmenting assessments of bioaccumulation potential. Modeling can provide a TMF-focused assessment for new or unreleased chemicals in the absence of field data by estimating TMF values and theoretical relationships between physical-chemical properties and TMF values (quantitative structure-activity relationships). An illustration of the use of physicochemical properties for estimating TMFs is provided. Overall, TMFs provide valuable information regarding bioaccumulation potential and should be incorporated into regulatory decision making following the suggestions outlined in this article.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.036
GPT teacher head0.237
Teacher spread0.202 · 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