Tritium plume observations in Lake Huron: measurements of dispersion and comparison of lateral plume structure with the normal distribution of the Gaussian Plume Model
Bibliographic record
Abstract
This study describes observations of the near-shore transport and dispersion of routine but intermittent tritium emissions from the Bruce Nuclear Generating Station into Lake Huron, which are used as a tracer for inferring dispersion parameters. Study region covers 45 km along the shore northward, 38 km southward, and 18 km off the shore. Currents were recorded by GPS drifters deployed in the study region, and tritium concentrations in the effluent plume were collected both along drifter trajectories and across them. Two episodes were analysed: one with the plume going into the headwind and one with a regular wind-driven plume. In situ dispersion was calculated from tritium plume observations. The highest concentrations encountered at each distance from the source were combined to locate the plume centreline and deduce scaling (dependence on the distance to the source) according to a power-law fit to these concentrations. Lateral distribution of concentration in the plume was Gaussian Plume Model parameterised with this in situ dispersion and then applied to remaining sampling locations, that is, to all off-centreline observations to model predictions. The headwind episode yielded a scaling exponent of dispersion α = 0.711 and magnitude S y = 11.3, and the episode of a regular wind-driven plume yielded α = 0.411 and S y = 25.9. Gaussian Plume Model from SRS-19 was deployed with these parameters. Across the analyzed region model predictions were characterized by R 2 = 0.34 and Pearson r = 0.36 during the headwind episode and R 2 = 0.72 with r = 0.71 during the regular wind-driven episode.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".