Environmental System Effects when Including Scrap Preheating and Surface Cleaning in Steel Making Routes
Bibliographic record
Abstract
Agriculture has the potential to supply large amounts of biomass for renewable energy production from residues from traditional crop production and from dedicated energy crops.This renewable energy production has significant potential to contribute to the reduction of GHG emissions in the energy sector by using ethanol and biodiesel to displace petroleum based liquid fuels and direct burning of biomass to displace coal for generating electricity.To quantify this biomass potential, we used the Canadian Economic and Emissions Model for Agriculture to estimate renewable energy production from biomass and the impact on agricultural production.We used two scenarios: the first scenario that looks at a combination of market incentives and mandates, and a second scenario that looks at only market incentives.The results show that: in the markets and mandates scenario, biomass production is higher, both ethanol and electricity are required to take place and land use change occurs.Agriculture has significant potential to generate biomass for energy under different scenarios, the incentive mix can have a large impact on the type of bioenergy produced, there is significant potential for GHG emission reductions and there is potential for unintended GHG effects, such as the increased clearing of land for crop production.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".