Forest biomass supply chain optimization for a biorefinery aiming to produce high-value bio-based materials and chemicals from lignin and forestry residues: a review of literature
Bibliographic record
Abstract
Technological development has enabled the production of new value-added products from lignocellulosic residues such as lignin. This has allowed the forest industry to diversify its product portfolio and maximize the economic returns from feedstock, while simultaneously working towards sustainable alternatives to petroleum-based products. Although previous research has explored industrial-scale production opportunities, many challenges persist, including the cost of woody biomass and its supply chain reliability. While numerous studies have addressed these issues, their emphasis has traditionally been on bioenergy, with little focus on biochemical, biomaterials, and bioproducts. This review seeks to address this gap through a systematic study of the work recently reported by researchers. A lot of work has been published from United States and Canada with an emphasis on bioenergy production (84.8%), 4.6% of the work is focused on biomass to materials and chemicals, and 10.6% addressed both. Between 2012 and 2015, the majority of published research focused on biomass to materials and chemicals and both biomass to energy and biomass to materials and chemicals. This fact highlights recent interests in diversified biorefinery portfolios. However, further work concerning forest biomass supply chain optimization and new high-value bio-based materials and chemicals is necessary.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".