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Record W6889804113 · doi:10.26071/02c0aa6b-2043-4461

Microplastic abundance in Placentia Bay, Newfoundland (2021)

2024· dataset· en· W6889804113 on OpenAlexaffabout

Bibliographic record

VenueOGSL repository · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMicroplasticsBayPlanktonSedimentAbundance (ecology)Plastic pollution

Abstract

fetched live from OpenAlex

Microplastics are plastic particles ranging from 1 µm to 5 mm. We studied the microplastic concentration in surface waters as well as the sediment of Placentia Bay, Newfoundland, Canada. Surface water samples were collected with a Plankton as well as a Neuston net at nine stations and sediment samples were collected with Van-Veen grab at six stations within the Bay in the summer of 2021 and microplastics >300 µm were isolated. The isolated microplastics were counted and categorized by size, shape, and color. Microplastic polymer composition was identified on a representative subsample by Raman microspectroscopy. This dataset is divided into the individual visual counts for the Plankton net samples, the Neuston net samples, and the sediment samples. Additionally, the Raman microspectroscopy identification is listed separately. This project is part of the Coastal Environmental Baseline Program Initiative under the Oceans Protection Plan of Fisheries and Oceans Canada.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.182
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.009
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.012

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 head0.005
GPT teacher head0.237
Teacher spread0.232 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2024
Admission routes2
Has abstractyes

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