Stand development and regeneration during a 33‐year period in a seral <i>Picea glehnii</i> forest, northern Japan
Bibliographic record
Abstract
Abstract Stand development and regeneration were studied during a 33‐year period (1965‐1998) in a 1‐ha plot in a seral Picea glehnii forest in northern Japan. P. glehnii was mono‐dominant in the upper canopy layer, but its understory trees were rarely found in 1965. Other species were scarcely observed in 1965. Many recruited saplings of Abies sachalinensis which had grown to > 5 cm diameter at breast height (DBH) by 1998 had become dominant in the understory layer. Mortality of P. glehnii canopy trees was low. Therefore, the stand basal area increased during the census period due to the growth of surviving canopy trees. Stand development brought about intense competition among trees by increasing local crowding for each tree, and promoted dominance of larger trees and suppression of smaller trees. Although growth rates of understory trees of the two conifers decreased with the increase in local crowding, the growth rate of A. sachalinensis was consistently higher than that of P. glehnii at all extents of local crowding. The recruitment rate (growing to ≥ 5 cm DBH) of the two conifers was less affected by local crowding. However, the number of recruits of P. glehnii was only about a quarter of that of A. sachalinensis during the census period because the regeneration of P. glehnii was largely restricted to fallen logs and within 1 m of the base of any live tree > 20 cm DBH. Therefore, our long‐term study suggests that A. sachalinensis will dominate over P. glehnii in the seral forest because of higher recruitment and growth rates of the former than the latter in the understory.
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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 | 0.001 | 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".