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Record W4389051853 · doi:10.5376/ijms.2023.13.0004

Microplastic Pollution in the Coast of Tarragona

2023· article· en· W4389051853 on OpenAlexvenueno aff
Isabel Pellicer

Bibliographic record

VenueInternational Journal of Marine Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsMicroplasticsSeawaterEnvironmental scienceMediterranean seaPollutionBuoySampling (signal processing)OceanographyMediterranean climateMarine pollutionGeographyGeologyEngineeringEcologyArchaeology

Abstract

fetched live from OpenAlex

The Institut Rambla Prim, in collaboration with the Institut-Escola del Treball of Barcelona, conducted a study on marine microplastics present in the seawater along the coast of Tarragona (Balearic Sea, Western Mediterranean), specifically in the towns of l'Ampolla and Altafulla, as well as in the sand of Altafulla beach. The study involved collecting water samples using the passive filtering prototype SB-Buoy, analyzing them in the laboratory, and manually sieving the beach sand, as a citizen science project conducted by students and teachers from professional degrees. The concentrations observed varied considerably depending on the sampling locations and periods. Significant preliminary results should be highlighted: tiny microplastics dominate the samples from seawater (Ø < 3 mm), and plastic pellets in the sand accounted for 52% of the anthropogenic waste by weight in the sampling area.

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 score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.009
GPT teacher head0.244
Teacher spread0.234 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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