Implications for Deriving Regional Fossil Fuel CO<sub>2</sub> Estimates from Atmospheric Observations in a Hot Spot of Nuclear Power Plant <sup>14</sup>CO<sub>2</sub> Emissions
Bibliographic record
Abstract
Using Δ14C observations to infer the local concentration excess of CO2 due to the burning of fossil fuels (ΔFFCO2) is a promising technique to monitor anthropogenic CO2 emissions. A recent study showed that 14CO2 emissions from the nuclear industry can significantly alter the local atmospheric 14CO2 concentration and thus mask the Δ14C depletion due to ΔFFCO2. In this study, we investigate the relevance of this effect for the vicinity of Toronto, Canada, a hot spot of anthropogenic 14CO2 emissions. Comparing the measured emissions from local power plants to a global emission inventory highlighted significant deviations on interannual timescales. Although the previously assumed emission factor of 1.6 TBq(GWa)-1 agrees with the observed long-term average for all CANDU reactors of 1.50 ± 0.18 TBq(GWa)-1. This power-based parameterization neglects the different emission ratios for individual reactors, which range from 3.4 ± 0.82 to 0.65 ± 0.09 TBq(GWa)-1. This causes a mean difference of-14% in 14CO2 concentrations in our simulations at our observational site in Egbert, Canada. On an annual time basis, this additional 14CO2 masks the equivalent of 27–82% of the total annual FFCO2 offset. A pseudo-data experiment suggests that the interannual variability in the masked fraction may cause spurious trends in the ΔFFCO2 estimates of the order of 30% from 2006–2010. In addition, a comparison of the modeled Δ14C levels with our observational time series from 2008–2010 underlines that incorporating the best available 14CO2 emissions significantly increases the agreement. There were also short periods with significant observed Δ14C offsets, which were found to be linked with maintenance periods conducted on these nuclear reactors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".