Uncertainty talk for bio-digital technologies: Expert conceptions of uncertainties in genomic selection for forestry
Bibliographic record
Abstract
The burgeoning literature on uncertainty analysis shows the need for accessible and transparent information about the limitations of knowledge associated with predictive models for environmental decision-making. Using qualitative analysis, we examine how experts involved in the development of genomic selection (GS) for Canadian public forestry conifer breeding assess and communicate uncertainty. GS is a bio-digital technology characterized by big data compilation, sophisticated statistical analysis, and high-throughput genome sequencing. While GS applications in forestry have the potential to increase yields, reduce errors, and improve the selection of resilient trees in the face of climate change, our data revealed barriers that impede more comprehensive discussions about uncertainty, including assumptions that uncertainty can (and should) be eliminated through the availability of more data, tacit commitments to the application of GS in commercial forestry operations, deterministic assumptions about linear gene-to-trait outcomes, and difficulties discussing uncertainty in collective settings. Uncertainty talk is uncomfortable as it can be perceived as a threat to applied research goals, but uncertainty talk is also a necessary, productive, and generative way to encourage transdisciplinary and inclusive discussions at early stages of predictive model deployment for environmental applications.
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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 | 0.062 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| 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".