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Record W7030088126

Microplastics in coastal marine habitats and food webs

2021· dissertation· en· W7030088126 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2021
Typedissertation
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMicroplasticsMarine habitatsSampling (signal processing)Marine debrisHabitatSeawaterEcotoxicologyShellfishMarine speciesFood chain
DOInot available

Abstract

fetched live from OpenAlex

Microplastic particles (MPs) are widely distributed in aquatic environments and present a potential risk to marine life. This thesis considers several issues relating to methodologies for sampling and analyzing MPs and the sources and fate of these particles in the marine environment, wild and farmed shellfish, and food webs of southern coastal British Columbia, Canada. 
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\nChapter 1 introduces MPs as a contaminant, methodologies for studying them, and reviews what is known about their source, fates, and ecotoxicology in marine environments. Chapter 1 also outlines the goals of this thesis.
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\nChapter 2 compares the potential MP concentration estimates provided by two different seawater sampling methods. Jar samples filtered to 8-µm yielded MP concentrations averaging approximately 8.5 times higher than bucket samples filtered to 63 µm, per L of water (at the site level), driven largely by differences in the number of microfibres. An analysis of MP concentrations and mesh sizes reported in the literature suggests that using a 300–350-µm mesh may underestimate total MP concentrations by one to four orders of magnitude compared with samples filtered through much smaller mesh sizes (e.g., less than 100 µm), and despite the effect of sample volume. Particles less than 300 µm in diameter make up a large component of MPs commonly found in fish and invertebrates. As such, common sampling practices fail to adequately measure a biologically relevant class of MPs, thereby undermining the ability to quantify ecological risk. 
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\nChapter 3 determines the influence of shellfish aquaculture activity, and its use of plastic equipment, on MPs in bivalves and their environment by comparing MP concentrations in Manila clams (Venerupis philippinarum) and Pacific oysters (Crassostrea gigas) grown on commercial shellfish beaches with those grown on non-aquaculture beaches from six areas. MP concentrations in water and sediment were also determined in four of the areas. MP concentrations did not differ between shellfish aquaculture and non-aquaculture sites for either bivalve species or for sediment and water samples. Beach sediment type had a minor effect, with more gravelly or sandier beaches associated with higher MP concentrations in oysters or clams, respectively. Oysters on sites using many synthetic anti-predator nets had more MPs than those on sites without any plastic, but analysis of particles using Fourier-transform infrared spectroscopy suggested a predominance of textile fibres including nylon and polyester, which are not typically used in shellfish aquaculture. 
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\nChapter 4 uses stable isotope food web analysis and hierarchical Bayesian generalized linear mixed models to explore whether bioaccumulation and biomagnification are occurring in coastal marine food webs at three locations. Bioaccumulation was higher for smaller-bodied suspension feeding animals such as bivalves. However, biomagnification was not occurring in animal digestive tracts, and trophic dilution was demonstrated in fish livers. Trophic transfer was shown to occur between prey and predator for rockfish, but higher concentrations in full stomachs compared with empty ones suggested rapid excretion of ingested MPs.
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\nChapter 5 supplies some general conclusions on the status of MPs in the British Columbian environment, as well as risks to seafood consumers. It also explores future work that will be needed to understand the complex ecotoxicology of MPs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
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 integrity0.0000.001
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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