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Record W2622423187 · doi:10.1139/cjfas-2016-0333

Elevated radioactive contamination from the Fukushima nuclear power plant accident in aquatic biota from a river with a lake in its upper reaches

2017· article· en· W2622423187 on OpenAlexvenueno aff
Mayumi Yoshimura, Akio Akama

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsnot available
FundersSumitomo Foundation
KeywordsEnvironmental scienceAlgaeContaminationBiotaEnvironmental chemistryNuclear power plantHydrology (agriculture)RadionuclideAquatic ecosystemEcologyBiologyChemistryGeology

Abstract

fetched live from OpenAlex

Five years have passed since the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident occurred. Forests, streams, and lakes remain radioactively contaminated, with slight sign of convergence. The radiocesium concentration of brown trout (Salmo trutta) in Lake Chuzenji (160 km from the FDNPP) still exceeds the Japanese regulatory limit of 100 Bq·kg −1 , likely due to elevated contamination in Lake Chuzenji. In this study, the concentration of 134 Cs and 137 Cs in algae, litter, sand substrate, and aquatic insects in a river originating from Lake Chuzenji (Daiya site) and in a nearby river (Watarase site) from 2013 to 2015 were compared. At the Daiya site, 134 Cs and 137 Cs concentrations of algae and aquatic insects were high (e.g., 137 Cs in algae: 160 Bq·kg −1 at the Watarase site and 320 Bq·kg −1 at the Daiya site) and still increasing in some groups such as Perlodidae and Heptageniidae, though the mean air dose rate (0.05 μSv·h −1 ) was lower than that at the Watarase site (0.11 μSv·h −1 ). We attributed this to high flow out of higher 134 Cs and 137 Cs concentration originating from Lake Chuzenji. Thus, lower reaches fed by contaminated headwaters will likely experience prolonged contamination.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.941

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.204
Teacher spread0.188 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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