Delineation of Three‐Dimensional Well Capture Zones for Complex Multi‐Aquifer Systems
Bibliographic record
Abstract
The delineation of well capture zones is a basic component of ground water protection. The conventional methodology for capture zone delineation is backward advective particle tracking, often applied under the assumption of a two-dimensional aquifer. The suitability of the conventional approach for complex heterogeneous multi-aquifer systems was investigated, using the Waterloo Moraine aquifer system as an example. It was found that the conventional approach produces irregular particle tracks that require judgment to interpret in a meaningful way, and it can raise questions that may affect the credibility of the capture zone delineation. As an alternative, the potentially powerful but little-used backward-in-time advective-dispersive transport approach was investigated. A key advantage of this approach is its capability to represent local heterogeneities through the dispersion term. The dispersion process has a natural smoothing effect that results in unambiguous capture zones without the need for interpretation, thus enhancing credibility. The question of capture zone validation is also addressed. The meaning of a three-dimensional capture zone is considered, and it is shown that a fully three-dimensional representation of the system is crucial for valid results. The distinction between the maximum extent capture zone and the surface capture zone is also explained. In the case of complex heterogeneous systems, advective particle tracking can be used as an initial screening tool, whereas the more realistic backward-transport modeling approach can be used for final capture-zone delineation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".