Clonal structure and diversity of <i>Cryptomeria japonica</i> along a slope in a cool-temperate, old-growth mixed forest in the snowy region of Japan
Bibliographic record
Abstract
To clarify patterns of clonal growth along a slope for Cryptomeria japonica (L.f.) D. Don, which can regenerate by layering resulting from snow pressure, we analyzed the spatial genetic structure with respect to slope position and in relation to stem size in a cool-temperate, old-growth mixed forest in the snowy region of Japan. For the genetic analysis, five polymorphic microsatellite markers developed for C. japonica were used. Spatial autocorrelation analyses revealed a significant positive association of ramets (trees) with the same genotype as the result of clonal growth at around <6 m regardless of slope position for understory trees (≥50 cm stem length and <10 cm diameter at breast height (DBH)). This clonal patch size almost corresponded to the clustering scale for overstory trees (≥10 cm DBH) belonging to the same genet. For understory trees in three subplots established along a slope, the size distribution of ramets within a clone (genet) followed an inverse J-shaped distribution without small ramets being distributed peripherally. These results suggest that each clone of C. japonica is maintained continuously in a relatively restricted area where it establishes, regardless of slope position, which could contribute to the high clonal and genetic diversity of C. japonica in this forest.
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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 | 0.001 |
| 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) | 0.000 | 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".