Analysis of Canada’s water use: tracing water flow from source to end use
Bibliographic record
Abstract
Freshwater is a critical natural resource and fundamental to social and environmental activities, including industrial activities, food production, and residential needs. Hence, it is important to understand provincial water supply and demand. However, there are large gaps in provincial and sectoral water use data. This study provides estimates for disaggregated water use by regional subsectors and uses Sankey diagrams to depict the water flow from intake to consumption and discharge. The study uses a bottom-up method in the oil and gas and hydropower sectors and top-down methods in the residential, commercial and institutional, manufacturing, mining, agricultural, and power sectors. Surface and ground water are considered separately. Water use in the year 2017 was analyzed for British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec, the Atlantic Provinces, and the Territories. Water-use intensities were also calculated by region and sector. A total of 40 billion m3 of water use is traced from source to either discharge or consumption. New disaggregated data is developed provincially and by sector for oil and gas, mining, and power generation. Water use in the oil and gas sector was disaggregated into 5 subsectors, with oil sands surface mining in Alberta as the largest consumer with 138 million m3 of water consumed. Hydro power was estimated to consume the most water out of all sectors, with 3393 million m3 of water consumed. Alberta was also found to have the largest consumptive water use per capita. The results provide important insights on water supply and demand in Canada. Such information supports both regional and federal governments in formulating appropriate regional and sectoral policies and can support water managers and the public in understanding water supply and demand in Canada. Modelling efforts requiring regional and sectoral water use can also use these results. Supplemental data for this article is available online at http://dx.doi.org/.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| 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) | 0.002 | 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".