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
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Bibliographic record
Abstract
Using Δ 14 C observations to infer the local concentration excess of CO 2 due to the burning of fossil fuels (ΔFFCO 2 ) is a promising technique to monitor anthropogenic CO 2 emissions. A recent study showed that 14 CO 2 emissions from the nuclear industry can significantly alter the local atmospheric 14 CO 2 concentration and thus mask the Δ 14 C depletion due to ΔFFCO 2 . In this study, we investigate the relevance of this effect for the vicinity of Toronto, Canada, a hot spot of anthropogenic 14 CO 2 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 14 CO 2 concentrations in our simulations at our observational site in Egbert, Canada. On an annual time basis, this additional 14 CO 2 masks the equivalent of 27–82% of the total annual FFCO 2 offset. A pseudo-data experiment suggests that the interannual variability in the masked fraction may cause spurious trends in the ΔFFCO 2 estimates of the order of 30% from 2006–2010. In addition, a comparison of the modeled Δ 14 C levels with our observational time series from 2008–2010 underlines that incorporating the best available 14 CO 2 emissions significantly increases the agreement. There were also short periods with significant observed Δ 14 C offsets, which were found to be linked with maintenance periods conducted on these nuclear reactors.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it