MétaCan
Menu
Back to cohort

Fukushima Daiichi–Derived Radionuclides in the Ocean: Transport, Fate, and Impacts

2016· review· en· W2414127386 on OpenAlexafffund
Ken O. Buesseler, Minhan Dai, Michio Aoyama, Claudia R. Benitez‐Nelson, Sabine Charmasson, Kathryn A. Higley, Vladimir Maderich, Pere Masqué, Paul J. Morris, Deborah Oughton, J. N. Smith

Bibliographic record

VenueAnnual Review of Marine Science · 2016
Typereview
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsBedford Institute of Oceanography
FundersMinisterio de Economía y CompetitividadGeneralitat de CatalunyaAgence Nationale de la RechercheNorth Pacific Marine Science OrganizationInternational Atomic Energy AgencyEuropean CommissionGobierno del Principado de AsturiasGordon and Betty Moore Foundation
KeywordsRadionuclideEnvironmental scienceOceanographyOverheating (electricity)SeawaterNuclear powerFukushima Nuclear AccidentEarth scienceNuclear power plantGeologyEcology

Abstract

fetched live from OpenAlex

The events that followed the Tohoku earthquake and tsunami on March 11, 2011, included the loss of power and overheating at the Fukushima Daiichi nuclear power plants, which led to extensive releases of radioactive gases, volatiles, and liquids, particularly to the coastal ocean. The fate of these radionuclides depends in large part on their oceanic geochemistry, physical processes, and biological uptake. Whereas radioactivity on land can be resampled and its distribution mapped, releases to the marine environment are harder to characterize owing to variability in ocean currents and the general challenges of sampling at sea. Five years later, it is appropriate to review what happened in terms of the sources, transport, and fate of these radionuclides in the ocean. In addition to the oceanic behavior of these contaminants, this review considers the potential health effects and societal impacts.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations309
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueAnnual Review of Marine ScienceSame topicRadioactive contamination and transferFrench-language works237,207