RETRACTED CHAPTER: Spatio-Temporal Optimization of Groundwater Monitoring Network at Pickering Nuclear Generating Station
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Through more than 400 wells, tritium leakage of Pickering Nuclear Generating Station has been monitored seasonally since 1999. Sampling and maintenance of monitoring wells being costly, it is required limiting number of samples while ensuring monitoring objectives. This study aims at proposing a geostatistical approach for sample reduction while meeting the monitoring objectives. So, four objectives were defined: (i) Geographical coverage. (ii) Denser sampling where tritium variability is high. (iii) Delimiting the threshold of 300 kBq/L, also (iv) the cut-off of 3000 kBq/L. These objectives were quantified using geostatistical measures, served as cost functions in heuristic optimization algorithms, implemented using scripting capacity of Isatis.neo software. The algorithm was successful in sampling optimization of 2010Q4 (fourth season) according to the first, third and fourth measures. The first measure selects spatially evenly distributed wells, necessary for unknown leakages. The third and the fourth geostatistical measures suggest sampling around a decisive tritium concentration. Considering these objectives, 22 samples were removed (totally 50 samples) while deterioration of characterization accuracy is negligible. Temporal variogram revealed tritium correlation over three years. So previously acquired samples could be used to improve the monitoring in scarcely sampled areas. But sensitivity analysis showed that samples older than four seasons do not improve the current season contamination characterization. The conditional optimization was applied to samples of 2010 (all seasons). Previous samples improved the first and the last geostatistical measure, while deteriorated the second and the third measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".