Mapping genetic variation and seed zones for<i>Bromus carinatus</i>in the Blue Mountains of eastern Oregon, USAIn this article, mention of companies or trade names does not constitute an endorsement of any product or procedure.
Bibliographic record
Abstract
Seed transfer zones ensure that germplasm selected for restoration is suitable and sustainable in diverse environments. In this study, seed zones were developed for mountain brome ( Bromus carinatus Hook. & Arn.) in the Blue Mountains of northeastern Oregon and adjoining Washington. Plants from 148 Blue Mountain seed source locations were evaluated in common-garden studies at two contrasting test sites. Data on phenology, morphology, and production were collected over two growing seasons. Plant traits varied significantly and were frequently correlated with annual precipitation and annual maximum temperature at seed source locations (P < 0.05). Plants from warmer locations generally had higher dry matter production, longer leaves, wider crowns, denser foliage, and greater plant height than those from cooler locations. Regression models of environmental variables with the first two principal components (PC 1 and PC 2) explained 46% and 40% of the total variation, respectively. Maps of PC 1 and PC 2 generally corresponded to elevation, temperature, and precipitation gradients. The regression models developed from PC 1 and PC 2 and environmental variables were used to map seed transfer zones. These maps will be useful in selecting mountain brome seed sources for habitat restoration in the Blue Mountains.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".