Overcoming technological, commercial, organizational and social uncertainties of innovation: The case of forest biomass as a replacement of petroleum-based feed stocks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it