Cold Hardiness Testing for Douglas-Fir Tree Improvement Programs: Guidelines for a Simple, Robust, and Inexpensive Screening Method
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Operational methods are needed for screening genotypes in breeding programs for adaptive traits. In this article, we present a detailed description of one procedure for screening improved coastal Douglas-fir seedlings and saplings for cold hardiness, based on research results of the Pacific Northwest Tree Improvement Research Cooperative. Artificial freeze testing of detached shoots from genetic tests, followed by visual scoring of injury, has proved to be an efficient, reliable, and cost-effective method of screening large numbers of genotypes. Relevant research results are summarized, and practical details of this methodology are presented for straightforward implementation by Douglas-fir breeders and researchers. West. J. Appl. For. 15(3):129-136.
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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 | 0.001 | 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 | 0.000 | 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 it