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The influx of marine debris from the Great Japan Tsunami of 2011 to North American shorelines

2018· article· en· W2782880633 on OpenAlexafffund
Cathryn Clarke Murray, Nikolai Maximenko, Sherry M. Lippiatt

Bibliographic record

VenueMarine Pollution Bulletin · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsNorth Pacific Marine Science OrganizationFisheries and Oceans Canada
FundersNational Oceanic and Atmospheric AdministrationMinistry of EnvironmentNorth Pacific Marine Science OrganizationNational Aeronautics and Space Administration
KeywordsDebrisShoreMarine debrisOceanographyGeographyFisheryGeologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Marine debris is one of the leading threats to the ocean and the Great East Japan Earthquake and tsunami on March 11, 2011 washed away an estimated 5million tons of debris in a single, tragic event. Here we used shoreline surveys, disaster debris reports and ocean drift models to investigate the temporal and spatial trends in the arrival of tsunami marine debris. The increase in debris influx to surveyed North American and Hawaiian shorelines was substantial and significant, representing a 10 time increase over the baseline in northern Washington State where a long term dataset was available. The tsunami event brought different types of debris along the coast, with high-windage items dominant in Alaska and British Columbia and large, medium-windage items in Washington State and Oregon. Recorded cumulative debris landings to North America were close to 100,000 items in the four year study period. The temporal peaks in measured shoreline debris and debris reports match the ocean drift model solutions. Mitigation and monitoring activities, such as shoreline surveys, provide crucial data and monitoring for potential impacts should be continued in the future.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.196
Teacher spread0.190 · 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; both teacher heads agree on what is shown here.

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

Citations98
Published2018
Admission routes2
Has abstractyes

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