The role of GIS and expert knowledge in 3-D modelling, Oak Ridges Moraine, southern Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it