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Record W2612886511 · doi:10.1139/facets-2017-0008

Microplastics in the gastrointestinal tracts of fish and the water from an urban prairie creek

2017· article· en· W2612886511 on OpenAlexafffundvenueabout
Samantha H. Campbell, Patrick R. Williamson, Britt D. Hall

Bibliographic record

VenueFACETS · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Regina
FundersMinistry of EnvironmentMinistry of Environment - Saskatchewan
KeywordsMicroplasticsEnvironmental scienceEffluentEnvironmental chemistryWastewaterFish <Actinopterygii>ContaminationWater pollutionPollutionFisheryEnvironmental engineeringEcologyBiologyChemistry

Abstract

fetched live from OpenAlex

Microplastics are defined as any plastic with a diameter ≤5 mm. Problems associated with these plastics such as contamination of both marine and freshwater environments and ingestion by aquatic organisms are of increasing concern. Our study quantifies the number of microplastics in a prairie creek immediately downstream of Regina, Saskatchewan, Canada. Water samples and five species of fish were collected from sample sites upstream and downstream of a wastewater treatment plant (WWTP) in the summers of 2015 and 2016. Samples were digested in either a Fe(II)/H 2 O 2 or NaClO solution and observed under a microscope where plastics present were enumerated by colour and type. At least one microplastic was detected in 73.5% of fish and 95.6% of water samples, showing that the creek does, in fact, contain microplastics. Concentrations were higher in water from upstream sites, likely due to dilution of creek water by the release of treated effluent. The results of this study provide baseline conditions for the presence of plastics in the creek prior to a major upgrade of the WWTP scheduled for completion in 2016.

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)0.000
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 score0.134
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.220
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 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

Citations212
Published2017
Admission routes4
Has abstractyes

Explore more

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