The role of GIS and expert knowledge in 3-D modelling, Oak Ridges Moraine, southern Ontario
Bibliographic record
Abstract
A basin analysis approach is used to help understand a complex aquifer system in the Oak Ridges Moraine and Greater Toronto areas, southern Ontario, Canada. The aquifer complex consists of a sequence of discontinuous strata that have a prominent regional unconformity. To help visualize this architecture, a stratigraphic database has been developed and used to construct a 3-D stratigraphic model, through selective integration of disparate data. To accurately interpret borehole logs, geological context was supplied by using expert knowledge constrained with a conceptual stratigraphic framework. Utilizing a digital stratigraphic training framework derived from manually coded, high-quality data, an expert system automatically interpreted and coded a large number of low-quality water well records. The expert system was designed to emulate the manual borehole interpretation process by applying knowledge-based geological rules, within the constraints of the digital training framework. Issues of poorly constrained interpolation due to sparse data are addressed by the integration of additional spatial rules defined by thematic map coverages within the expert system. As quantitative hydrogeological modelling moves to more regional scales, geological knowledge input becomes increasingly more valuable. The availability of seamless geological mapping improves 3-D modelling and helps to limit the effect of deficiencies in data coverage and data quality, often encountered in regional hydrogeological studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".