Overcoming technological, commercial, organizational and social uncertainties of innovation: The case of forest biomass as a replacement of petroleum-based feed stocks
Bibliographic record
Abstract
The replacement of petroleum-based feed stocks with more environmentally sound alternatives has gained widespread interest in a number of industries. Sustainably managed, forest biomass can be a key driver in this transition by providing a source of biofuels, chemical feedstocks and lignin-based polymers. New genomic and metagenomic approaches can identify novel enzymes that will allow for example the degradation of lignocellulose and the discovery and development of biocatalysts for improving production efficiencies and reducing environmental impacts such as carbon emissions. However, in addition to these technical hurdles that must be overcome, the transition to more environmentally sound biomass-based industrial systems will depend on legitimization processes to overcome commercial, organizational and social uncertainties, and will affect various industrial sectors differently. This paper presents preliminary insights from the Genome Canada funded project ‘Harnessing Microbial Diversity for Sustainable use of Forest Biomass Resources’, which explores such genomic and metagenomic approaches for improved biomass efficiencies. As part of the study, we examine commercialization processes, public policy issues and secondary stakeholder concerns of this technology to better understand how such technologies may be successfully diffused. We discuss the implications for industry sectors and other stakeholders affected by the development of this technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.004 |
| 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".