Bibliographic record
Abstract
Ontario's Ministry of the Environment and Climate Change (ministry) has a dataset of over 750,000 well records from 1899 to present with a growth rate of 15,000 to 20,000 records per year. Well records provide information about a well's location, construction, lithology and pumping test, as prescribed by the Wells Regulation. In general, a well record is required to be submitted by the well contractor at the time of construction, major alteration or decommissioning of water supply wells, test holes and dewatering wells. Since its inception, the provincial well record dataset has been an invaluable resource for geologists, engineers, and other stakeholders across the province as it is the primary source of province wide subsurface geological and groundwater information. It is made available through the ontario.ca website as a Data Catalogue and an Interactive Well Records Map. Data on the well records are of varied quality with some well records providing a high degree of accuracy and completeness, and others not. During use of the well record data by external stakeholders, professional geoscientists and engineers have assessed and corrected the data, and added new data to produce "value-added" well record datasets for their areas of study. They have checked for and interpreted for regional consistency in nomenclature, location, etc. In November 2016, the ministry initiated the Well Record Enhancement Project to determine how to create, maintain and present an authoritative provincial well record dataset in which the geoscience community has confidence and can contribute ongoing improvements. WSP Canada Inc. was retained to assist with the delivery of this project. The objectives of the project are to identify and assess options to 1) enhance the quality of Ontario's well record data, including the integration of existing value-added datasets; 2) identify how to validate/curate corrections and value added data from external sources into a "best available" dataset on an ongoing basis; 3) enhance how well record and value-added data are made available to the public, including improved functionality/presentation and delivery models; and 4) potentially recover the costs of enhancements and delivery. Recommendations are to be based on a solid understanding of the needs and capacity of the MOECC, other existing well record users and creators of existing value added datasets including professional geoscientists, engineers and well contractors. To this end, the ministry is consulting with key stakeholders to better understand what enhancements they need and want in terms of well record quality, access, presentation and delivery. In addition the ministry is examining what corrected/value added data may be available for a curated authoritative provincial dataset, and what role, if any, stakeholders would like to play in its delivery.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.075 |
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".