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Distribution of microplastics in bathyal- to hadal-depth sediments and transport process along the deep-sea canyon and the Kuroshio Extension in the Northwest Pacific

2023· article· en· W4387394913 on OpenAlexaff
Masashi Tsuchiya, Tomo Kitahashi, Ryota Nakajima, K. Oguri, Kiichiro Kawamura, Akimu Nakamura, Kengo Nakano, Yosaku Maeda, Masafumi Murayama, Sanae Chiba, Katsunori Fujikura

Bibliographic record

VenueMarine Pollution Bulletin · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsNorth Pacific Marine Science Organization
FundersMinistry of EnvironmentEnvironmental Restoration and Conservation Agency
KeywordsAbyssal zoneBathyal zoneOceanographyGeologyCanyonSink (geography)Deep seaAbyssal plainBaySeafloor spreadingSubmarine canyonContinental shelfSedimentGeomorphologyBenthic zone

Abstract

fetched live from OpenAlex

Understanding microplastic (MP) behavior in oceans is crucial for reducing marine plastic pollution. However, the complex process underlying MP transportation to the deep seafloor remains unknown despite the deep sea being considered its major sink. We focused on MP distribution in Sagami Bay (adjacent to highly populated areas of Japan), the plate triple junction connected through the Sagami Trough, and the abyssal plain immediately below the Kuroshio Extension. We observed the highest number of MPs in the abyssal stations, more than previously reported. The polymer types and aspect ratio of MPs in the abyssal stations significantly differed from those in the bathyal/hadal stations. The study suggests that MPs accumulated in the open ocean surface layer sink to the abyssal plains immediately below it, while MPs from land sources accumulate in the bathyal depth and are transported to the hadal depth near the coast through turbidity currents along the submarine canyon.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.197
Teacher spread0.192 · 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 teacher head, 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

Citations45
Published2023
Admission routes1
Has abstractyes

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