Priority Actions to Improve Provenance Decision-Making
Bibliographic record
Abstract
Selecting the geographic origin—the provenance—of seed is a key decision in restoration. The last decade has seen a vigorous debate on whether to use local or nonlocal seed. The use of local seed has been the preferred approach because it is expected to maintain local adaptation and avoid deleterious population effects (e.g., maladaptation and outbreeding depression). However, the impacts of habitat fragmentation and climate change on plant populations have driven the debate on whether the local-is-best standard needs changing. This debate has largely been theoretical in nature, which hampers provenance decision-making. Here, we detail cross-sector priority actions to improve provenance decision-making, including embedding provenance trials into restoration projects; developing dynamic, evidence-based provenance policies; and establishing stronger research–practitioner collaborations to facilitate the adoption of research outcomes. We discuss how to tackle these priority actions in order to help satisfy the restoration sector's requirement for appropriately provenanced seed.
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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 | 0.480 | 0.513 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".