Tracking local radiocarbon releases from nuclear power plants in southern Ontario (Canada) using annually-dated tree-ring records
Bibliographic record
Abstract
Understanding the radiocarbon (14C) content of atmospheric carbon dioxide (CO2) underlies many fields of research. This study shows how one can track the influence of nuclear power plants at the local and regional scale in Canada’s largest urban area. This area is subject to significant 14CO2 depletion due to CO2 emissions from fossil fuel burning. Tree cores collected across southern Ontario in 2018 show that tree-rings dated annually record the same decadal trends as atmospheric measurements at a background site. Tree cores taken closer to 14CO2 or fossil fuel sources reflect those local influences. Data of 14C from a site 20 kilometer downwind from a nuclear power plant is highly correlated (R2=0.76) with annually reported emissions from the plant for 2009-2018. Extending the analysis back to the 1990s shows that the emissions of 14CO2 were 4-8 times higher than those at present. At that time, 14CO2 emissions were sufficiently strong to affect a remote background monitoring site. Concerning the urban fossil fuel CO2 emission signature, 14CO2 in the cellulose of a tree from downtown Toronto has an extremely depleted Δ14C signature. The local signal from traffic emissions overshadows any 14CO2 from nuclear emissions in the region. This study suggests that, with more cores to reflect the emissions of nuclear 14CO2 before 1990 and more suitable urban sampling locations (i.e., representative of a neighborhood rather than one road), this approach has potential to better track the long-term impact of urbanization and nuclear power plants in Canada, and potentially elsewhere around the globe.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".