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Record W2145558577

Bioamplification as a bioaccumulation mechanism

2013· article· en· W2145558577 on OpenAlexaff
Jennifer M. Daley

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMechanism (biology)BioaccumulationBusinessChemistryEnvironmental chemistryEpistemologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Persistent organic pollutant (POP) bioaccumulation models have been generally formulated to predict two main processes, bioconcentration and biomagnification. A third bioaccumulation process that can mediate chemical potential in an organism is bioamplification. Bioamplification occurs when an organism loses body weight and chemical partitioning capacity faster than it can eliminate contaminants. Bioamplification causes an increase in chemical fugacity in the animal's tissues and results in the redistribution of contaminants from inert storage sites to more toxicologically sensitive tissues. Further, bioamplification generally occurs when an organism experiences major bioenergetic bottlenecks or nutritional stress, frequently associated with critical periods in the animal's life history. The goal of this dissertation was to characterize bioamplification as a general bioaccumulation process that is additive to bioconcentration and biomagnification mechanisms of chemical exposure. Empirical studies validating bioamplification in three different animal models each undergoing a recognizable bioenergetic bottleneck during their life history were completed. Specifically, bioamplification was validated in emergent aquatic insects, fish embryos during egg development and larval fish. Bioamplification factors in the above studies ranged from 1.9-2.1 in emergent male mayflies, 1.8-5.4 in incubating yellow perch embryos and 1.5-5.3 in larval Chinook salmon (dependent on food resource availability). To complement these studies, a literature review was completed to demonstrate the wide applicability of this concept to different animal species. Examples of bioamplification were presented in invertebrates, fishes, birds and mammals corresponding to bioenergetic bottlenecks related to migration, reproduction, early life stages, metamorphosis, over wintering weight losses and disease. Bioamplification factors summarized in the literature ranged from 1.1-14 and were similar in magnitude to biomagnification factors typically reported for aquatic and terrestrial organisms. While most of the descriptions of bioamplification in the literature have treated it as a bioaccumulation curiosity, the results of this dissertation demonstrate that bioamplification is a general bioaccumulation process that contributes to enhanced chemical fugacities of POPs across the animal kingdom. Further, the results of this dissertation showed that bioamplification is producing maximum POP fugacities at critical periods over the animal's life history and as such the consequences of bioamplification may be very important to wildlife hazard and risk assessments.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.216
Teacher spread0.200 · 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 designTheoretical or conceptual
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

Citations2
Published2013
Admission routes1
Has abstractyes

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