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Record W3135675237 · doi:10.5194/essd-13-2555-2021

Radionuclide contamination in flood sediment deposits in the coastal rivers draining the main radioactive pollution plume of Fukushima Prefecture, Japan (2011–2020)

2021· article· en· W3135675237 on OpenAlexaff
Olivier Evrard, Caroline Chartin, J. Patrick Laceby, Yuichi Onda, Yoshifumi Wakiyama, Atsushi Nakao, Olivier Cerdan, Hugo Lepage, Hugo Jaegler, Rosalie Vandromme, Irène Lefèvre, Philippe Bonté

Bibliographic record

VenueEarth system science data · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsAlberta Environment and Protected Areas
FundersJapan Society for the Promotion of ScienceCentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsRadionuclideEnvironmental scienceSedimentPlumePollutionRadioactive wasteHydrology (agriculture)Radioactive contaminationNuclear power plantFlood mythOceanographyGeologyPhysical geographyGeographyArchaeologyMeteorologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract. Artificial radionuclides including radiocesium (134Cs and 137Cs) and radiosilver (110mAg) were released into the environment following the Fukushima Dai-ichi nuclear power plant accident in March 2011. These particle-bound substances deposited on soils of north-eastern Japan, located predominantly within a ∼3000 km2 radioactive fallout plume and drained by several coastal rivers to the Pacific Ocean. The current dataset (Evrard et al., 2021), which can be accessed at https://doi.org/10.1594/PANGAEA.928594, compiles gamma-emitting artificial radionuclide activities measured in 782 sediment samples collected from 27 to 71 locations across catchments draining ∼6450 km2 during 16 fieldwork campaigns. These campaigns were conducted in Japan between November 2011 and November 2020 in river catchments draining the main radioactive plume. This database may be useful to evaluate and anticipate the post-accidental redistribution of radionuclides in the environment and for the spatial validation of models simulating the transfer of radiocesium across continental landscapes.

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)0.003
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 score0.502
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.234
Teacher spread0.219 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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