Topography and planting density affect understory plant diversity in conifer plantations through development of stand canopy closure
Bibliographic record
Abstract
Sustainably managed plantations not only provide high-quality timber but also contribute to biodiversity. Clarifying the direct and indirect effects of planting density and topography on the development and diversity of understory vegetation is important from the perspective of the diversity-retaining function of young plantations. This study conducted a vegetation survey in plantation stands of Japanese cedar, 4–7 years after planting. These plantations varied in initial planting density (ranging 1000–3000 trees/ha), and contained topographic variations from ridges to lower slopes. The direct and indirect effects of initial planting density and topography on species composition, diversity, and height growth of understory vegetation were investigated. Topographic variation affected stand canopy closure and the mean height growth rates of planted cedars. Topography effect was directly related to vegetation height. High initial planting density accelerated stand canopy closure through crown development of planted trees. Through stand canopy closure, initial planting density and topography indirectly affected plant diversity, although their direct effects were not significantly detected. Initial planting density and topography impacted vegetation diversity mainly through the degree of canopy closure, rather than directly. These findings are useful when planning reforestation schedules and spatial designs within the landscape while taking diversity into consideration.
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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 | 0.001 |
| 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) | 0.001 | 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".