Multi‐spatial analysis of forest residue utilization for bioenergy
Bibliographic record
Abstract
Abstract The alternative energy sector is expanding quickly in the USA since passage of the Energy Policy Act of 2005 and the Energy Independence and Security Act of 2007. Increased interest in wood‐based bioenergy has led to the need for robust modeling methods to analyze woody biomass operations at landscape scales. However, analyzing woody biomass operations in regions like the US Inland Northwest is difficult due to highly variable terrain and wood characteristics. We developed the Forest Residue Economic Assessment Model (FREAM) to better integrate with Geographical Information Systems and overcome analytical modeling limitations. FREAM analyzes wood‐based bioenergy logistics systems and provides a modeling platform that can be readily modified to analyze additional study locations. We evaluated three scenarios to test the FREAM’s utility: a local‐scale scenario in which a catalytic pyrolysis process produces gasoline from 181 437 Mg yr−1 of forest residues, a regional‐scale scenario that assumes a biochemical process to create aviation fuel from 725 748 Mg yr−1 of forest residues, and an international scenario that assumes a pellet mill producing pellets for international markets from 272 155 Mg yr−1 of forest residues. The local scenario produced gasoline for a modeled cost of $22.33 GJ−1*, the regional scenario produced aviation fuel for a modeled cost of $35.83 GJ−1 and the international scenario produced pellets for a modeled cost of $10.51 GJ−1. Results show that incorporating input from knowledgeable stakeholders in the designing of a model yields positive results. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".