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Record W4321995289 · doi:10.5194/egusphere-egu23-9817

Modeling the influence of biogeochemical processes on the transport of microplastics in the Arctic Ocean

2023· preprint· en· W4321995289 on OpenAlexaboutno aff
Anfisa Berezina, Evgeniy Yakushev, Philip Wallhead, André Staalstrøm

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsBiogeochemical cycleOceanographyArcticEnvironmental scienceMicroplasticsDetritusZooplanktonWater columnMarine ecosystemEcosystemGeologyEcologyBiology

Abstract

fetched live from OpenAlex

Microplastics (MPs) are plastic fragments less than 5 mm in size. At present, all natural environments, including the World Ocean, are polluted with MPs from the poles to the equator.The study tests the hypothesis that seasonality of processes in the marine ecosystem can affect the vertical transport of MPs in the water column (through changes in density due to biofouling and absorption and excretion of MP particles by zooplankton). The work used the biogeochemical model OxyDep (Yakushev et al., 2011) to reproduce the seasonality of marine ecosystem. BioPlast (Berezina et. al. 2021) considers processes of MPs degradation, biofouling, ingestion of particles by zooplankton and MPs in detritus. OxyDep biogeochemical module and MP module BioPlast were coupled with ROMS-20 (20 km resolution) 3D transport model using the FABM framework (Bruggeman & Bolding, 2014). The applied models describe the transformation of MPs and reproduce in detail the effect of ecosystem and biogeochemical processes on its vertical and horizontal transport, as well as on its burying in sediments.MPs were supplied to the Arctic Ocean from rivers flowing into the White, Barents, Kara and East Siberian Seas, as well as from the Atlantic Ocean, excluding any other sources. It was shown that within 8 years MPs spreads throughout the Arctic Ocean, except for the Canadian Basin. Several numerical experiments were conducted, MPs of Atlantic origin dominate in the western part of the Arctic Ocean. According to the upper estimate, the MPs from the North Atlantic occupies almost the entire water area of the Arctic Ocean. At the same time, the concentrations of MP covered with biofilm, MP in zooplankton, and MP in detritus are quite low compared to the virgin form of MP. A short-term purification of the surface layer from MP due to biofouling and sedimentation with detritus was observed in the estuarine zones and in the Fram Strait. This is partly due to the low productivity of the Arctic Ocean and the short period of phytoplankton blooms, and partly to the limitations of the BioPlast model. Further work will be devoted to the analysis of other sources of microplastics in the Arctic Ocean, such as the supply of MPs through the Bering Strait and local sources associated with maritime activity on the Northern Sea Route.The development of the described model is of great importance, especially for the Arctic region, where regular observations of MPs are not available, and the ecosystem is extremely vulnerable to anthropogenic impact.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.022
GPT teacher head0.222
Teacher spread0.200 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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