Forest genomics research and development in Canada: Priorities for developing an economic framework
Bibliographic record
Abstract
Forest genomics is a relatively recent research field and is often poorly understood both by the public and forest managers. Genomics in forestry, an expansion of forest biotechnology, seeks to develop generalized technologies for use in industrial plantations and/or natural forests as well as within process optimization, product development and international trade facilitation. With such tools it is possible to address formerly intractable issues such as understanding the underpinnings of complex traits for conservation management purposes, improved use of forest trees as carbon sinks, feedstock for biofuels and “green chemistry” through deeper understanding and effective utilization of forests’ natural variation. Diverse end-users could benefit from genomics tools; for example, real-time detection and mapping of known and novel pathogens along with risk assessments to protect forest nurseries and natural forests from invasive pathogens and reduce economic losses associated with diseases. Since 2001, there has been approximately $123 million invested in Canadian forest genomics research; we thought it would be helpful to summarize projects in Canada and the USA and to identify research priorities and potential economic implications by: (a) developing a robust typology of forest sector genomics research relevant to Canadian application; (b) categorizing each initiative for its application potential (commercial, noncommercial); and, (c) demonstrating with silvicultural gain, insect resistance, and wood composition themes the application of modeling and economic analysis.
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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".