Geospatial distribution of chemical, bacteriological and gas parameters in southern Ontario groundwater
Bibliographic record
Abstract
Groundwater Resources Study 17 is a recent publication by the Ontario Geological Survey that delineates wide regions in southern Ontario wherein individual chemical constituents are elevated in groundwater including arsenic, barium, boron, fluoride, nuisance gases (methane, hydrogen sulphide, hypoxic gas), iodide, nitrate, chloride, selenium and uranium. With several exceptions, these regions are explainable as combinations of the natural influence of (1) bedrock lithology, (2) marine sediments, (3) glacial sediment thickness and (4) bedrock topography. Marine influence, particularly in eastern Ontario, is apparent in the distribution of chloride, sodium, iodine, boron, selenium and methane. Drift thickness and/or bedrock topography influences the distribution of chloride, selenium, methane, barium and to a lesser extent, iodine. Bedrock lithogeochemistry controls, or partly controls, the distribution of arsenic, selenium, barium, uranium and chloride. Shales and carbonate rocks of Devonian age host groundwater that is almost universally elevated in fluoride, making this the only constituent that is spatially related to the age of the host formations. Nitrate is one of several mapped parameters that shows human influence in its distribution; which combines with the influences of coarse grained glacial sedimentary cover and karst in bedrock. The spatial incidence of fecal and total coliform bacteria is also discussed in GRS 17. The occurrence of karst appears to an overwhelming factor in the distribution of coliform bacteria in bedrock. Another anthropogenic influence is chloride from road salting, which has a widespread but intermittent distribution. It can be easily differentiated from natural chloride by comparing molar ratios against those for bromide. The data used to generate the polygons in GRS-17 were derived from the OGS Ambient Groundwater Geochemistry database.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".