Integration of 'golden spike' geologic and hydrogeological data sets
Bibliographic record
Abstract
High resolution geological data sets have increasingly been collected and used in the context of groundwater mapping programs in Ontario. This has provided much more robust geological conceptual models for key areas in the province. At the same time, many advances have been made in hydrogeology to enable acquisition of high-resolution hydraulic data in vertical profile. Here we present a few examples from Ontario to demonstrate how the collection of these two types of datasets in tandem can provide a hydraulically-calibrated, geologic framework to generate a robust, 3-D hydrogeologic model. While the geological framework remains the key to extrapolation between boreholes, the hydraulic significance of the various stratigraphic (sub)units and sedimentary features are identified and quantified with the direct measurement of hydraulic conditions at multiple depths and locations. These data sets are very much complementary and provide corroborating evidence and robustness to define the geometry, thickness and position of key 3-D hydrogeological units that control the groundwater flow system and contaminant pathways and transport rates.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".