Impact of biofuel production on water demand in Alberta.
Bibliographic record
Abstract
The production of biofuels (e.g., ethanol and biodiesel) requires a significant amount of water during feedstock production, transportation, and its conversion into biofuels. Therefore present study devoted to study the impact of biofuel production on water demand in Alberta. In scenario #1, it is assumed that ethanol is produced from both wheat and wheat straw and that biodiesel is produced from rapeseed. Scenario #2 proposes ethanol production from wheat only and biodiesel production from rapeseed. The water requirements for biofuel production in both scenarios are calculated for Alberta for the year 2025. Data on the current availability of water in Alberta indicate that the Athabasca, North Saskatchewan, and Peace River basins of northern Alberta have enough water to grow crops for the production of biofuels. In 2025, Alberta will have to produce 3,754 million liters of ethanol and 270 million liters of biodiesel to meet the projected levels. If biofuels are produced from the crops grown in the above-mentioned northern river basins, the province of Alberta should be able to meet biofuel demand in 2025 sustainably. The water requirement from these river basins for biofuel production will increase to 5.2%, 0.6%, and 11.6%, respectively, of the natural flow in scenario #1 whereas, for scenario #2, the water requirement from these rivers basins will increase to 5.2%, 2.3%, and 16.1%, respectively, of natural flow. These increases in the requirements are much lower than the possible allowed withdrawal levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".