Compilation of nuclear industry's gaseous radiocarbon emissions from 1950 to 2023
Bibliographic record
Abstract
The dataset consisting of 17 files compiles worldwide available nuclear gaseous 14C emissions (inorganic, organic and total) from 1950 to 2023. We use the European Commission RAdioactive Discharges Database (RADD, 2025) compiling the annual radioactive discharges of the European nuclear power plants (NPPs) and we include additional data sources, such as the Discharges of Radionuclides to the Atmosphere and the Aquatic Environment (DIRATA) database (IAEA, 2025) and institutional reports, in particular from the United Nations Committee on the Effects of Atomic Radiation (UNSCEAR) and the International Atomic Energy Agency (IAEA). Moreover, we consider publications and numerous annual plant-specific environmental reports providing 14C emission values for NPPs. In total, 14C emission values of nuclear facilities operating in Argentina, Bulgaria, Brazil, Canada, China, Czech Republic, Finland, France, Germany, Hungary, Japan, Lithuania, Romania, Slovakia, Slovenia, South Korea, Spain, Sweden, Switzerland, The Netherlands, United Kingdom, and the USA are presented. Beside annual 14C emissions, monthly, quarterly, and weekly values are also compiled for some nuclear facilities. If not otherwise mentioned, details about the data sources are available in the column Comments of the files.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.024 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".