Implementation of marker-assisted selection in BC forests : perception survey
Bibliographic record
Abstract
The iconic forests of British Columbia are deeply rooted in the lives of its inhabitants. Known for its lush green landscape and old growth trees, BC forests are home to over 1,300 plant and animal species, and provide a playground for recreational activities. The forest industry is also a key contributor to the provincial economy. Over the last decade, the forest sector has experienced a number of challenges as a result of the global economic crisis, the US housing crash, changing markets, low-cost competitors, the strength of the Canadian dollar, and the mountain pine beetle epidemic. Since, the provincial and federal governments have made a commitment to transform the forest sector through innovation, enhanced environmental performance, and new markets. One such area of innovation has been in forest genomic technologies. Marker-assisted selection (MAS) is a biotechnological tool that allows desired traits to be flagged on the genome. This tool may assist tree breeders with the early selection of preferred genotypes, thus reducing the breeding cycle and more accurately and efficiently selecting for improved qualities. However, there is a poor understanding of perceived acceptability towards the adoption of this technology. The objectives of this research were to investigate how the implementation of marker-assisted selection is perceived by forest stakeholders and First Nations in BC, and if this perception is dependent on the context of implementation. To accomplish these objectives, a mixed methods research approach was taken, employing semi-structured individual interviews, followed by a Likert scale questionnaire. Participants were categorized into four groups: government, industry, environmental non-governmental organizations (ENGOs), and First Nations. The results of this analysis found that government and industry participants held positive perceptions towards MAS, supporting its use and continued research in BC. Both agreed that the advantages of MAS outweigh the disadvantages, frequently identifying its benefits in forest regeneration and to tree breeders. ENGOs and First Nations demonstrated a less favourable attitude towards MAS. Their attitudes lie between neutral and negative. Concerns were most strongly focused on environmental impacts, ecosystem degradation, and reduced genetic diversity; while identified benefits were specific to tree breeders and improved tree resiliency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".