Proposed research on social perception of marker-assisted selection and its role in the forests of British Columbia
Bibliographic record
Abstract
The forest industry is a major player in the provincial economy, provides a significant contribution to government revenue, and accounts for 3% of British Columbia’s GDP. However, with the reduction of housing starts in the US in 2006, the economic crisis of 2008, a steady decline in newsprint demand, and the Mountain Pine Beetle epidemic, the provincial and federal governments have searched for ways to transform the forest industry through innovation, improved environmental performance, and new markets. One such investment has been in marker-assisted selection (MAS), which is a genomic-based biotechnological tool that allows desired traits to be flagged on the genome. Since MAS is a new genomic tool to the forest industry, it is necessary to survey silviculture stakeholders in BC on their perception of this resource to tree breeders, its perceived use, and the context for which it should be implemented. If it is a tool whose implementation is perceived positively, it would significantly reduce the cycle of the tree breeding process, as it allows for the early selection of genotypic traits. Moreover, it would allow tree breeders to more efficiently and accurately select for improved wood qualities, growth rates, and resistance to pests, diseases, and climate change.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".