Spatial Variations in Tap Water Isotopes Across Canada: Tracing Water from Precipitation to Distribution and Assess Regional Water Resources
Bibliographic record
Abstract
With global warming and increasing water use, tap water resources need sustainable management. We used hydrogen and oxygen isotope measurements (𝛿2H and 𝛿18O) to identify issues associated with tap water resources in Canada. We analyzed 576 summer tap samples collected from across Canada and 76 tap samples from three cities during different seasons and years. We classified the samples based on their sources: groundwater (TapGroundwater), river (TapRiver) and lake (TapLake). 𝛿2H in tap water correlates strongly with values predicted for local precipitation across Canada with a stronger correlation for TapGroundwater and TapRiver than for TapLake. We then constructed water balance models to predict the 𝛿2H of surface water across Canada, and validated it against Canadian river water 𝛿2H data. 𝛿2H in tap water correlates strongly with values predicted for surface water across Canada with a stronger correlation for TapRiver and TapLake than for TapGroundwater. TapGroundwater 𝛿2H values reflect the 𝛿2H of annually averaged precipitation, whereas TapRiver and TapLake 𝛿2H values reflect post-precipitation processes. We used the 𝛿2H residuals between the observed and predicted 𝛿2H values to assess regional processes influencing tap water 𝛿2H values across Canada. Regionally, snow/glacier melt contributes to all tap sources around the Rockies. Tap waters are highly evaporated across Western Canada, irrespective of their sources. In the Great Lakes and East Coast regions, tap waters are evaporated in many localities, particularly those using surface reservoirs and lakes. This study provides baselines for isotopic monitoring of tap water resources and forensic studies in Canada.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".