Geostatistics of the Borden Aquifer: High‐Resolution Characterization Using Direct Groundwater Velocity Measurements
Bibliographic record
Abstract
Abstract In 1986, a seminal data set from the Canadian Forces Base (C.F.B.) Borden aquifer, Ontario, Canada, was published, illustrating, in unprecedented detail, the spatial distribution of hydraulic conductivity ( K ). Among many contributions attached to that data set was a geostatistical examination of field‐based data for comparison with theoretical predictions of macro‐dispersivity. However , that work treated K as a static parameter and the sole source of flow variability. Here, point velocity probes (PVPs) are used to extend the earlier work by collecting a novel high‐resolution data set of groundwater velocity ( v ) measurements in the C.F.B. Borden aquifer. Velocity is a dynamic parameter of fundamental importance, closely tied to solute dispersion. Over 400 velocity measurements were collected along a transect perpendicular to flow, analyzed geostatistically, and compared with the analysis of the B‐B′ cross‐section of K reported by Sudicky (1986), https://doi.org/10.1029/wr022i013p02069 . The PVP measurements exhibited geostatistics similar to those previously estimated by Sudicky (1986), https://doi.org/10.1029/wr022i013p02069 . This finding suggests, as implicitly assumed in the previous work, the distribution of v is primarily controlled by K . PVPs also provided a novel, high‐resolution data set of groundwater flow directions. Since the ultimate objective of aquifer characterization includes the definition of velocity fields, this work not only extends the 1986 work, but also demonstrates a viable alternative for characterizing flow patterns in aquifers—with the advantage that direct v measurements reflect variability in porosity and hydraulic gradient, as well as K . This could ultimately be advantageous at sites with greater heterogeneity or other dynamic hydrogeological variabilities more pronounced than those at the Borden site.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".