Seedlot Selection Tool and Climate‐Smart Restoration Tool: Web‐based tools for sourcing seed adapted to future climates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it