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Record W6920823642 · doi:10.6084/m9.figshare.16964651

Analysis of Canada’s water use: tracing water flow from source to end use

2021· article· en· W6920823642 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWater useHydropowerWater supplyResource (disambiguation)Surface waterWater consumptionNon-revenue waterWater resourcesConsumption (sociology)

Abstract

fetched live from OpenAlex

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/.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.012
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.026
GPT teacher head0.195
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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