Evaluating Douglas Fir’s Provenances in Romania Through Multi-Trait Selection
Bibliographic record
Abstract
[Mirb.] Franco) is a valuable timber species native to western North America that was introduced to Europe in the 19th century. The objective of this study was to select the most valuable and stable Douglas fir provenances in Romania by combining growth and quality traits, using two indices recently used in forest tree species: the multi-trait genotype-ideotype distance index (MGIDI) and the multi-trait stability index (MTSI). The study was conducted across three common garden experiments in Romania, established in 1977, evaluating 61 provenances from the United States, Canada, Germany, France, and Romania. The analyzed traits were diameter at breast height (DBH), total height (TH), and pruned height (PH). Significant genotype-environment interactions were observed, with the Douglas fir showing superior growth performance in one of the testing sites in western Romania (Aleșd). The MGIDI and MTSI identified high-performing provenances from diverse geographic origins, including the Pacific Northwest, Europe, and Canada. Selection differentials ranged from 2.8% to 10.9% for individual traits, highlighting the potential for genetic improvement. The selected provenances represent valuable genetic resources of Douglas fir that are adapted to environmental conditions in the Carpathian region, contributing to the development of climate-adaptive breeding strategies and sustainable forest management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".