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Record W1819594892 · doi:10.1139/cjfas-2014-0170

Assessment of radiocaesium accumulation by hatchery-reared salmonids after the Fukushima nuclear accident

2014· article· en· W1819594892 on OpenAlexvenueno aff
Shoichiro Yamamoto, Kouji Mutou, Hidefumi Nakamura, Kouta Miyamoto, Kazuo Uchida, Kaori Takagi, Ken Fujimoto, Hideki Kaeriyama, Tsuneo Ono

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsnot available
Fundersnot available
KeywordsOncorhynchusHatcheryBiologyAnimal scienceFisheryMuscle tissueContaminationPelletsEnvironmental scienceFood chainFish <Actinopterygii>Ecology

Abstract

fetched live from OpenAlex

To understand the process of radiocaesium uptake in salmonids after the Fukushima Dai-ichi Nuclear Power Plant accident, a lake caging experiment and two captive-rearing experiments with controlled radiocaesium concentrations of water and feed were conducted in and around Lake Chuzenji, central Honshu Island, Japan (160 km from the station). Substantial accumulations of radiocaesium were confirmed in muscle of hatchery-reared kokanee (Oncorhynchus nerka) and masu salmon (Oncorhynchus masou) after release into the cages, indicating that radionuclide contamination of fish is an ongoing process, 1.5 years after the nuclear accident. Two captive experiments, controlling water and feed radiocaesium levels, showed that direct radiocaesium transfer from water (43 mBq·L –1 ) in Lake Chuzenji to muscle tissue was undetected, at least during the ∼90-day experimental period, whereas a rapid increase in radiocaesium levels was observed when fish were cultured using radiocaesium-contaminated pellets. The results revealed that radiocaesium contamination in salmonids is mainly via the food chain and that direct intake from water via the skin, gut, and (or) gills has no major direct impact on muscle tissue concentrations.

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)0.001
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 score0.033
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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