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Benthic marine debris in the Bay of Fundy, eastern Canada: Spatial distribution and categorization using seafloor video footage

2019· article· en· W2985478087 on OpenAlexafffundabout
Alexa J. Goodman, Tony R. ‎Walker, Craig J. Brown, Brittany R. Wilson, Vicki Gazzola, Jessica A. Sameoto

Bibliographic record

VenueMarine Pollution Bulletin · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsFisheries and Oceans CanadaBedford Institute of OceanographyDalhousie UniversityNova Scotia Community College
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsMarine debrisBenthic zoneDebrisPlastic pollutionBayEnvironmental scienceOceanographyFishingSeafloor spreadingMarine spatial planningMarine pollutionCommercial fishingFisheryMarine protected areaMicroplasticsGeologyPollutionHabitatEcology

Abstract

fetched live from OpenAlex

Marine debris, particularly plastic and abandoned, lost and discarded fishing gear, is ubiquitous in marine environments. This study provides the first quantitative and qualitative assessment of benthic debris using seafloor video collected from a drop camera system in the Bay of Fundy, Eastern Canada. An estimated 137 debris items km−2 of seafloor were counted, comprising of plastic (51%), fishing gear (including plastic categories; 28%) and other (cable, metal, tires; 21%). Debris was widespread, but mainly located nearshore (within 9 km) and on the periphery of areas with high fishing intensity. This baseline benthic marine debris characterization and estimate of abundance provides valuable information for government (municipal, provincial and federal) and for other stakeholders to implement management strategies to reduce plastic and other categories of benthic marine pollution at source. Strategies may include limiting plastic use and reducing illegal dumping through improved education among fishers.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.178
Teacher spread0.172 · 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.

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

Citations36
Published2019
Admission routes3
Has abstractyes

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