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Record W4311681333 · doi:10.22215/etd/2022-15143

Spatial and temporal variation of microplastics in the Ottawa River watershed with citizen science as a complementary sampling methodology

2022· dissertation· en· W4311681333 on OpenAlexafffundabout
Shaun A. Forrest

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsCarleton University
FundersMitacs
KeywordsMicroplasticsEnvironmental scienceTributaryWatershedHydrology (agriculture)SnowmeltChannel (broadcasting)SedimentSampling (signal processing)SnowGeographyOceanographyGeologyEngineering

Abstract

fetched live from OpenAlex

Microplastic concentrations were investigated in water and sediment in the Ottawa River watershed in Quebec and Ontario, Canada.Microplastic concentrations were measured temporally during precipitation events in an urban creek in the City of Ottawa and on the main channel of the Ottawa River within the Ottawa/Gatineau urban area.The temporal events sampled included heavy rainfall, snowfall and snow melt with microplastic concentrations measured from these events compared to concentrations during nonprecipitation events.The results showed sewage overflows contribute to large short-term inputs of microplastics to the Ottawa River, however, spring snowmelt presented the highest increase of microplastic concentration for both the urban creek and main channel of the river.Additionally, the research examined the spatial distribution of microplastics in river water throughout the Ottawa River at 105 sample points on the main channel and tributaries.An ANCOVA analysis demonstrated only two significant spatial factors related to microplastic concentration, with distance downstream from the river source and an increase of microplastics at boat launch locations.However, these were both only weak relationships.The research incorporated two citizen science projects to investigate microplastic concentration in water and sediment in the Ottawa River watershed, while evaluating the potential of citizen science as a complementary sampling tool for microplastic research.With robust project design and implementation, citizen science is an excellent complementary tool for examining microplastic concentration in freshwater environments as it can reduce research costs, while increasing spatial scope of microplastic projects.iii Additionally, increasing citizen science capacity in microplastic research and monitoring is a useful tool to engage volunteers and involve them in environmental education while contributing to advancing the understanding of microplastic pollution.

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 score0.072
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.028
GPT teacher head0.280
Teacher spread0.252 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

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