From Hindsight to Foresight: Biofuels in Agriculture and Forestry
Bibliographic record
Abstract
The Canadian forest sector is facing a time of significant transformation. In this brief we argue that regulatory analysis and foresight is an essential component of the successful transformation of the forest sector. As a first step to a regulatory analysis and foresight, we will illustrate here how to think about regulation based on the hindsight gleaned from the agricultural sector. Specifically we will derive lessons learned from the history of agricultural biofuels. A shift of energy production toward biofuels seems to support all of the three pillars of sustainable development. Accordingly, the most widely produced transport biofuel internationally, agricultural ethanol, has been marketed as a smart and green choice and as better alternative to oil-based gasoline. This image is now crumbling somewhat. We will focus on lessons learned from the agricultural biofuels story (paying special attention to the case of ethanol) and explore what hindsight can teach us.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".