Using Oil, Gas and Salt Resources Library well data in groundwater research
Bibliographic record
Abstract
Providing safe drinking water to all Ontarians is increasingly the focus of new regulation and initiatives. Models and studies require high-quality data sources or expensive data collection efforts. Data archived at the Oil, Gas and Salt Resources Library (OGSR Library) represents one possible source of data in groundwater modelling. The OGSR Library dataset has geological and hydrological data for depths in the subsurface that are not intercepted by water and geotechnical borings. This data can be used to create models that extend our knowledge and protection of groundwater deeper into the subsurface. The OGSR Library is a data archive and geoscience research centre focusing on the data associated with wells drilled under the Oil, Gas, and Salt Resources Act (OGSRA). This data includes files on 26,720 wells (40 Brine, 12,192 Gas Wells, 993 Private Gas Wells, 5,368 Oil Wells, 447 Storage Wells, 207 Solution Mining Wells, 412 Injection and Disposal Wells, 7,061 Other Types). Geophysical logs for 20,430 wells are available, drill cutting samples from 10,887 wells and core samples from 1,110 wells. Geological formation top picks have been recorded for 289,600 contacts, with ~33,861 of these contacts reviewed by QA/QC geologists at the OGSR Library. In addition to these data a huge effort has been undertaken to complete quality assessment and quality control (QA/QC) on the original data from drillers. In addition to geological and geophysical information there are 35,006 water contact records in the Library database. This data is collected through OGSRA well drilling where the reporting requires details about water interval depths, type, and static level. Detailed water analyses and chemistry are available on 1,023 of the water zones. There can be multiple water records for a single well depending on the number of water intervals encountered during drilling. This allows potential aquifers to be located and mapped for different water types. The use of this data has the capability of greatly improving our understanding of subsurface water at greater depths than other datasets. All detailed well record information can be accessed from the OGSR Library databases with a membership. A regional 3D geological model of the Paleozoic bedrock of southern Ontario is in its third year of development. This project is a collaboration by the Geological Survey of Canada (GSC), Ontario Geological Survey (OGS), Ontario Ministry of Natural Resources and Forestry (MNRF) and the OGSR Library. The next steps of this project will use the modelled formation layers generated from OGSR Library data to create a hydrostratigraphic model of Southern Ontario. This model represents one application of OGSRA well data to protecting groundwater, many others opportunities are possible.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".