An economic assessment of genomics research and development initiative projects in forestry.
Bibliographic record
Abstract
Abstract The field of forest genomics is rapidly expanding, and many new potential uses of the genetic information gained are being developed. Some of these uses are primarily economic in nature, such as increasing the growth rate of trees and increasing yields for woody biomass, or producing trees with more desirable physiological or wood characteristics. Other uses are additionally advantageous to ecological or social goals, such as pest resistant trees that can withstand the effects of insects or diseases. Yet, to date, no forest products company in Canada has embraced forest genomics into mainstream business activity. This could be due to a number of factors: the lack of familiarity with genomics tools, the lack of expertise to assess genomics within the industry, the costs of applying genomics techniques in tree breeding, the lack of evidence of industrial benefits and the lack of commercialization potential. Here, we conducted an economic assessment of seven forest genomics research projects in Canada, including value judgements on the potential of commercialization and research application. The outcome of our work allowed us to (1) categorize the projects by type including the description of the economic frameworks, (2) undertake an economic assessment of each of these projects, using qualitative and quantitative (if available) information and (3) provide advice and a value judgement on the necessary micro-level economic conditions for application and commercial success.
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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 | 0.042 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".