Seedlot Selection Tool and Climate‐Smart Restoration Tool: Web‐based tools for sourcing seed adapted to future climates
Bibliographic record
Abstract
Abstract The Seedlot Selection Tool and Climate‐Smart Restoration Tool are web‐based tools designed to match seedlots with planting sites assuming that seedlots are adapted to the past climates in which they evolved, primarily with respect to temperature and aridity. The tools map the climatic match of seedlots with the past or projected climates of planting sites. The challenge is that future climates are a moving target, which means that seedlots must be adapted to the near‐term climates as well as the climates of the mid‐ to late‐21st century. Because climate projections are uncertain, the prudent approach is to aim for the warmest climate that may be expected while ensuring that seedlots moved from warmer to colder locales are not moved so far that they risk cold damage. Uncertainty in climate projections may be mitigated by ensuring genetic diversity through mixing seed sources and having collections from many parents per seed source. Three examples illustrate how to effectively use the web tools: (1) choosing seedlots targeting different future climates for a mid‐elevation Douglas‐fir site in the Washington Cascades, (2) finding current and future seed sources for restoration of big sagebrush after fires in the Great Basin and Snake River Plain, and (3) planning to ensure that a Douglas‐fir seed inventory includes seedlots suitable for future climates in western Oregon and Washington.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".