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Record W4386619985 · doi:10.1051/e3sconf/202342404012

How Does Nuclear Wastewater Discharge Affect Fishery and Marine Environment: A Case Study of Japan

2023· article· en· W4386619985 on OpenAlexaff
Zijian Liu

Bibliographic record

VenueE3S Web of Conferences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsLangley Environmental Partners Society
Fundersnot available
KeywordsWastewaterEnvironmental scienceFishingRadioactive wasteNuclear powerWaste managementBusinessEnvironmental protectionFisheryNatural resource economicsEnvironmental engineeringEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

With the increasing use of nuclear energy, human lives have benefited from a variety of aspects since nuclear energy can produce carbon-free electricity. Nevertheless, governments must be cautious about the waste nuclear energy produces for it’s extremely harmful to the environment and has detrimental impacts on human health. Since the nuclear water at the Fukushima plant was released in the following years after 2011, both Japan and its neighboring countries were seriously affected. Some other coastal areas also have varying degrees of pollution depending on the ocean current. The extent of the impact of nuclear wastewater namely the affected areas and the diffusion of elements in nuclear wastewater will be shown in the paper. Additionally, this paper will analyze and elaborate on how nuclear wastewater can affect the marine environment due to the structure of the marine environment and the properties of nuclear wastewater. Lastly, the impact of nuclear wastewater on the fishery in Japan and neighboring countries will be discussed by showing data from relevant research papers. This paper will focus on the impact of nuclear wastewater on the marine environment and the vicinity fishing industry.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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