Estimating Canada's virtual water trade using an Input-Output framework
Bibliographic record
Abstract
This study investigated whether Canada's virtual water export exceeds the country's virtual water import replacements. The research was undertaken using Canadian commodity transactions and water use in 2011. To test the hypothesis, an ecologic-economic indicator namely virtual water trade was estimated. This indicator was estimated by integrating blue water into the Input-Output model. To analyze the trade-offs between economic and water use resulting from exports and import replacements in 2011, the amount of GDP generated to satisfy Canadian net exports was computed. To do an accurate estimation on Canada's virtual water trade in 2011, the volume of water used for hydro-electricity generation was added to the amount of water intake for thermal- electricity power generation.For a million dollars of traded commodities in 2011, Canada's virtual water exports, import replacements and net virtual water exports were estimated to be +1,386 KM^3, -1,117 KM^3 and +268 KM^3 respectively. This suggests that Canada was a net virtual water exporter in 2011: the direct plus indirect blue water required to produce one million dollars of Canadian exports in 2011 was greater than that used for production of one million dollars of imports; therefore, the hypothesis of this study was not rejected. Canada has a comparative advantage in exporting commodities that were more water-intensive compared to the country's import replacements. Since the hypothesis was based on the H-O theorem, it was also concluded that the classical trade theory can explain fresh water flows between Canada and its trading peers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".