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Quantification of marine macro-debris abundance around Vancouver Island, Canada, based on archived aerial photographs processed by projective transformation

2017· article· en· W2756077429 on OpenAlexafffundabout
Tomoya Kataoka, Cathryn Clarke Murray, Atsuhiko Isobe

Bibliographic record

VenueMarine Pollution Bulletin · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsNorth Pacific Marine Science OrganizationFisheries and Oceans Canada
FundersJapan Society for the Promotion of ScienceMinistry of the Environment, Government of JapanMinistry of EnvironmentCabinet Office, Government of JapanNorth Pacific Marine Science OrganizationRiver FoundationNew York State Department of Environmental Conservation
KeywordsDebrisShoreGeologyMarine debrisAerial photosAerial photographySatellite imageryAerial surveySatelliteSubmarine pipelineRemote sensingEnvironmental scienceOceanographyPhysical geographyGeography

Abstract

fetched live from OpenAlex

The abundance of marine macro-debris was quantified with high spatial resolution by applying an image processing technique to archived shoreline aerial photographs taken over Vancouver Island, Canada. The photographs taken from an airplane at oblique angles were processed by projective transformation for georeferencing, where five reference points were defined by comparing aerial photographs with satellite images of Google Earth. Thereafter, pixels of marine debris were extracted based on their color differences from the background beaches. The debris abundance can be evaluated by the ratio of an area covered by marine debris to that of the beach (percent cover). The horizontal distribution of percent cover of marine debris was successfully computed from 167 aerial photographs and was significantly related to offshore Ekman flows and winds (leeway drift and Stokes drift). Therefore, the estimated percent cover is useful information to determine priority sites for mitigating adverse impacts across broad areas.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.999

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)0.0020.000

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 head0.195
Teacher spread0.189 · 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 designNot applicable
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

Citations47
Published2017
Admission routes3
Has abstractyes

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