Wood-based bioenergy products — land or energy efficient?
Bibliographic record
Abstract
Woody feedstocks will play an important role in meeting the total demand for biomass to generate electricity and produce ethanol in the United States. We analyzed 186 different scenarios (31 rotation ages (10 to 40 years in annual time steps); two types of forest management (intensive and nonintensive); and three feedstocks (logging residues only, pulpwood only, logging residues and pulpwood combined)) for ascertaining relative savings in greenhouse gas (GHG) emissions of two wood-based energy products (electricity and ethanol) on per unit land and per unit energy bases with respect to equivalent fossil fuel based energy products. Relative savings in GHG emissions were higher under intensive forest management compared with nonintensive forest management on a per unit land basis, whereas this situation reverses on a per unit energy basis. Combined use of pulpwood and logging residues saved the highest amount of GHG emissions on a per unit land basis, but on a per unit energy basis, relative GHG savings were similar to when only logging residues were used as a feedstock. Existing policies promoting bioenergy development in the United States only consider GHG savings on a per unit energy basis. A need exists to consider GHG savings on a per unit land basis as well to ensure efficient utilization of existing land resources to mitigate GHG emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".