New Sustainability Perspectives on Pollutant Releases from Canada’s Nuclear Sector
Bibliographic record
Abstract
This novel characterization of new Canadian radionuclide release data aims to both deepen the understanding of the nature and magnitude of present-day emissions from nuclear facilities and accelerate the tracking of this sector's progress toward United Nations Sustainable Development Goal (SDG) 12 (responsible consumption and use patterns) and target 12.4 (environmentally sound chemicals management). Further novel perspectives on the role of this data as an indicator of sustainability are discussed by merging it with other pollutant releases from this sector, as reported to the National Pollutant Release Inventory (NPRI), to fill gaps in the latter's substance coverage. These public data sets are processed and analyzed using Tableau software and the Organization for Economic Cooperation and Development's framework for using pollutant release and transfer (PRTR) data in sustainability analysis. Findings confirm that radionuclide emissions to air and direct discharges to water from present-day Canadian nuclear facilities do not contribute significantly to national-scale radionuclide contamination. Moreover, findings validate the usefulness of combining various PRTR (and similar) data to address substance coverage gaps and set a global precedent for strengthening PRTR indicator power in SDG 12 evaluation. This work underscores the value of interoperable data in accelerating knowledge translation of PRTRs in the lens of sustainable development.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".