HYDROGEOLOGICAL CHARACTERIZATION OF A LEGACY WASTE STORAGE SITE AND THE CHALLENGE OF COMMUNICATING UNCERTAINTY
Bibliographic record
Abstract
Analytical groundwater and contaminant transport models rely on estimates of hydrogeological parameters that can range from two to three orders of magnitude. The effect parameter variability has on the results of groundwater and contaminant transport modelling was assessed for a legacy nuclear waste storage site in southern Ontario. Site specific hydrogeological parameters were estimated from groundwater measurements collected and hydraulic response testing completed at the Site. A 2D groundwater flow and contaminant transport model was developed and three hundred and seventy-five scenarios were modelled by manipulating hydraulic conductivity, dispersivity, and recharge estimates. The results indicate hydraulic conductivity, dispersivity, and recharge all effect contaminant breakthrough times and under/overestimate breakthrough by up to 50 years. The results of the sensitivity analysis exemplify and confirm that models are only ever tools to test potential outcomes and are limited in their ability to predict future scenarios. The model developed for the Site offers one line of evidence that advective transport of contaminants below waste storage area would be slow, but the model ignores the stratigraphic heterogeneity and geochemical processes that would influence the rate and distance contaminants travel. The inherent uncertainty of modelling results prompted research into how people interpret and respond to scientific uncertainty. There is a need for the ongoing research into the communication of scientific estimations and depoliticizing scientific results. Questions into how trust effects public buy-in and how to educate without overwhelming the public remain unanswered. Further research into how to effectively communicate scientific results and the inherent uncertainty is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 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".