Perceived Acceptability of Implementing Marker-Assisted Selection in the Forests of British Columbia
Bibliographic record
Abstract
The forest sector in British Columbia (BC) has faced a number of challenges over the past decade. In response to some of those challenges, the government has invested in forest genomic tools. Marker-assisted selection (MAS) is a biotechnological tool that flags desired traits on the genome. This tool may assist tree breeders with the early selection of preferred genotypes, reducing the breeding cycle and more accurately and efficiently selecting for improved qualities. However, there is a poor understanding of the perceived acceptability of implementing MAS. Semi-structured interviews and a questionnaire were employed across participants categorized into four groups. It was found that government and industry participants held positive perceptions towards MAS, supporting its use and continued research in BC, and identifying its benefits in forest regeneration and to tree breeders. Environmental non-governmental organizations (ENGOs) and First Nations attitudes lay 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. It was concluded that before MAS can be successfully implemented, an appropriate setting must first be established through improved knowledge of biotechnology and its applications, well-defined policies, and strengthened engagement and consultation with First Nations.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".