Developments in a surficial stratigraphic framework for 3D geological modelling
Bibliographic record
Abstract
Work is ongoing on the development of a framework for a Southern Ontario regional 3D surficial geological model. The focus in 2016-17 has been on data capture and web enabling for online viewing/download. This work builds on and complements the extensive database of subsurface information acquired over the last 15 years by the Ontario Geological Survey (OGS) as part of its surficial 3D mapping initiative. Recent coring programs in Simcoe County and the Niagara Peninsula have resulted in cores to bedrock being retrieved across much of the 'Golden horseshoe'. Continuous coring provided ground-truthing for over 100 line-kms of reflection seismic data recently collected in these areas. Legacy and archival datasets are also being added to complement the cored-borehole dataset. The 3D model is built on a provincial digital elevation model supplemented for Great Lakes by NOAA bathymetric data and for smaller lakes Canadian hydrographic field sheets (e.g., navigable waterways, Trent - Severn). Geological interpretations have been added from legacy high-resolution reflection seismic profiles in Lake Ontario (bedrock topographic elevation). The stratigraphic framework is additionally being enhanced by the capture of section descriptions and borehole logs from past OGS surficial mapping projects, integrated into a PostgreSQL database. Stratigraphic classification of Provincial Groundwater Monitoring Wells and data-mining from Source Water Protection technical reports will also inform the model as will data from the MOECC Water Well enhancement project. In addition, downhole geophysical and geochemistry frameworks will assist with stratigraphic classification. Downhole geophysical data can reduce reliance on continuous-core data for stratigraphic studies once adequate work has established an index 'fingerprint' for stratigraphic units. Consolidation of combined stratigraphic data in a PostgreSQL database supports serving this information online via Groundwater Information Network (GIN). GIN works in concert with parallel MOECC initiatives to support a distributed database framework for groundwater geoscience in Ontario.
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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 | 0.001 | 0.001 |
| 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 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".