Structure and developmental process of a<i>Quercus mongolica</i>var.<i>grosseserrata</i>forest in the<i>Fagetea crenatae</i>region in Japan
Bibliographic record
Abstract
Structure and developmental processes were studied in a Quercus mongolica Fisch. var. grosseserrata (Bl.) Rehd. et Wils. forest in the Fagetea crenatae Bl. region in Japan. The Quercus forest was classified into three stand types: stands dominated by Quercus with many species (type Q-MIX), Quercus-Fagus (type Q-F), and Quercus (type Q). In Q-MIX, Alnus hirsuta Turcz. had a bell-shaped DBH-class distribution. Most Quercus trees were single stemmed. The establishment of Quercus trees occurred continuously from the 1900s. The percentage of growth change (%GC) exhibited negative values from the 1940s. In Q-F and Q, Quercus trees had bell-shaped DBH-class distributions, and multiple-stemmed trees showed broad distributions. In Q-F, tree establishment peak was in the 1870s. %GC exhibited large fluctuations. In Q, tree establishment peak was in the 1850s. %GC exhibited negative values for 60 years. In conclusion, type Q-MIX, Q-F, and Q developed mainly by seedling regeneration following major cutting in the 1900s, sprout and seedling regeneration following intermittent cuttings mainly in the 1870s, and sprout and seedling regeneration following successive cuttings mainly in the 1850s, respectively. Cutting disturbance can be a major factor in developmental processes in Quercus forest; the frequency and intensity of cuttings affect the stand structure and the dominance of Quercus in the Fagetea crenatae region.
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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".