High-density, high quality regional sampling of water supply wells: Ontario's ambient groundwater geochemical program
Bibliographic record
Abstract
The Ambient Groundwater Geochemistry (AGG) initiative of the Ontario Geological Survey is a regional high density groundwater sampling program, the purpose of which is to map and understand the existing groundwater geochemical conditions in Ontario's major rock and surficial sediment aquifers. Throughout the last decade, the study has amassed data for 2664 samples from 2095 stations across 96,000 km2 representing all of southern Ontario. This one-time sampling program relies on existing well infrastructure sampled in a 10x10 km grid pattern. Monitoring and farm wells are used but the majority are domestic water supply wells with purging and sampling protocols adapted to the well type. Sites are randomly selected such that three criteria are met: (1) the water source must be determined, (2) the full, untreated geochemical matrix must be characterized (3) data quality must be assured; i.e. it must be demonstrated that what was intended to be measured has been correctly measured. A combination of field protocols, laboratory methods and a post acquisition QC auditing process, which collectively last for 6 months beyond a typical field season. Wells are selected only if their well construction details can be ascertained and cross-checked. The sources, and therefore reliability, of this information are recorded in the database and used later in an audit of all station information collected in the field. The audit, which uses well logs, field notes, well owner comments, continuous logs of field parameters (temperature, pH, etc) and field photos, typically lasts several months and scrutinizes well construction details, well-head security, plumbing details, integrity of water source and the geological origin of the water. In most years, based on the audit, a small number of sampled waters do not meet one of the three criteria and are rejected for inclusion in the AGG database. Analytical QC/QA procedures are rigorous. At least two analytical techniques are used to analyze many of the important parameters including the major ions, nitrate, iodide and many metals and these redundant analyses are checked against each other. Blind field duplicates, blanks and multiple reference standards are inserted at regular intervals in lab submissions and amount to 15% of all samples submitted and are used to confirm precision and accuracy for all parameters. Where data are found to fail the quality assurance tests, mitigation action is taken that may include re-analysis, resampling, or at worst, removal of the problem samples from the database. These techniques provide the quality assurance required for publication of the database. All blind quality control data are published, along with 27 station attributes, which allows end-users many options in the way they use the data, including creating subsets of the data for particular uses. The breadth of analysis, uniformity of coverage, areal extent and data quality of this dataset together far exceeds that of any previously existing groundwater geochemical databases in the province of Ontario.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".