Detection of quantitative trait loci for wood strength in<i>Cryptomeria japonica</i>
Bibliographic record
Abstract
Cryptomeria japonica D. Don (sugi) is one of the most important forest tree species in Japan. The progeny of a cross between the cultivars Iwao-sugi and Boka-sugi were analyzed using RAPD markers, with the pseudo-test-cross strategy, to construct linkage maps of the parental cultivars. A total of 355 segregating loci were detected among 72 offspring: 200 and 155 markers being distributed in Iwao-sugi and Boka-sugi, respectively. In Iwao-sugi, 119 markers with confirmed map positions were assigned to 21 linkage groups covering 1756.4 cM. In Boka-sugi, 84 markers with confirmed map positions were assigned to 14 linkage groups covering 1111.9 cM. The framework map distance in Iwao-sugi and Boka-sugi provides about 62 and 40% coverage, respectively, of the total genome, estimated to be approximately 2800 cM in length. Using genetic linkage maps constructed in this study, 15 QTLs were detected that are associated with the modulus of elasticity (MOE), an important indicator of wood strength. The QTLs for MOE explained about 45% of its total phenotypic variance. Some QTLs associated with different phenotypic traits were located on the same linkage groups. Some of the QTLs for MOE measured by two different methods (the hanging method and the tapping method) were located independently on the different linkage groups.
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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".