A quantification and analysis of historical sectoral and regional water withdrawals in Canada
Bibliographic record
Abstract
Canada is a water rich country with one of the highest annual water uses per person among developed countries. This study provides a systematic, comprehensive analysis of recent data on this water use at national and subnational scales. It spatially disaggregates surveyed data from Statistics Canada (StatCan) to develop a historical dataset from 2005-2018 for Canadian water withdrawals at provincial and river basin scales for seven water use sectors: domestic use, manufacturing, irrigation, livestock, mining, oil and gas and thermal power generation. Additionally, sectoral water withdrawals are estimated for each province-river basin combination. Sectoral priorities are analyzed at the provincial and river basin scales and historical trends are identified. Water use intensity indicators are calculated and compared between different provinces, and a water stress index is used to identify regions most prone to water shortages. We find that water use decreased nationally over the study years for all sectors except irrigation, mining and oil and gas. Ontario had the highest water use of all provinces, mainly for thermal power generation. Manufacturing and domestic sectors were the dominant users in Quebec and British Columbia while the Prairies had more diversified uses. Domestic water use per capita values in Newfoundland & Labrador and Quebec are higher than the national average and all global values included in the study for comparison. Finally, the irrigation sector withdraws the most water per $GDP nationally while the oil and gas sector withdraws the least. Dataset development faced challenges related to data availability and uncertainties in downscaling assumptions. These challenges are described, and emphasize the need for a systematic and standardized approach to water data gathering and sharing in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".