Redefining the Water-Food-Energy Nexus for Biofuels: How to Mitigate the COVID-19 Pandemic Effects in Canada
Bibliographic record
Abstract
Ensuring access to affordable, reliable, sustainable and modern energy for all, is a recognized Sustainable Development Goal. Yet the COVID-19 pandemic poses great challenges to the provision of bioenergy in Canada. Our study aims to examine these challenges by applying the Water-Food-Energy (WEF) Nexus Approach and suggest an alternative policy framework in the post-COVID-19 recovery process. The paper analyzes the socio-economic and environmental impacts of COVID-19 and draws upon the bioenergy management strategies and policies in Canada, as well as other countries. The revised policy framework is built by considering the interactions across the WEF Nexus and adopting the experience from international examples, which could effectively minimize the shortcomings of the existing Canadian policy framework. Decision-makers may use our framework to overcome challenges created by the COVID-19 pandemic and ensure smooth bioenergy development and provision amid this global crisis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".