The economic effect of genomic technology on the forestry industry
Bibliographic record
Abstract
In response to threats from climate change, such as an increased likelihood of droughts and insect outbreaks, significant investments in forestry genomics research have been made. The main advantage of genomic technology is that it greatly reduces the amount of R&D time to come up with a new product, and it is much more precise than traditional breeding techniques. However, the technology also comes with higher upfront R&D costs. Thus, whether the research effort would result in a worthwhile use of scarce research resources remains unknown. To help quantify the economic effect, we assess the welfare consequences of the forestry genomic research by estimating a timber supply model and a dynamic global forest products trade model. Using the forest industry of Alberta as our empirical setting, we find that the research program can yield an increase in total economic surplus of 400 million CAD in present value and the benefit-cost ratio of the research program is 43.9, indicating that more resources can be allocated advantageously to genomics-assisted tree breeding programs. The findings provide a justification for adopting genomic technology in the forestry sector and are useful in supporting genomics-enhanced reforestation policies and investment decisions. Acknowledgement : We acknowledge cash funding for this research from Genome Canada, Genome Alberta through Alberta Economic Trade and Development, Genome British Columbia, the University of Alberta and the University of Calgary. Further cash funding has been provided by Alberta Innovates BioSolutions, Forest Resource Improvement Association of Alberta, and the Forest Resource Improvement Program through West Fraser Ltd. and Weyerhaeuser Timberlands. In-kind funding has been provided by Alberta Agriculture and Forestry, Blue Ridge Lumber West Fraser, Weyerhaeuser Timberlands Grande Prairie, and the Thomas, Wishart, and Erbilgin labs in support of the Resilient Forests (RES-FOR): Climate, Pests & Policy Genomic Applications project.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".