Properties of stumps that promote the growth and survival of Japanese cedar saplings in a natural old-growth forest
Bibliographic record
Abstract
In a natural forest that has experienced logging and lacks large fallen logs, cut stumps are expected to become a suitable substrate for regeneration. We investigated the properties of stumps that promoted the natural regeneration of coniferous species in a natural old-growth forest dominated by Cryptomeria japonica (L.f.) D. Don. A comparison of C. japonica saplings growing in three microsites (ground, stumps, and fallen logs) revealed that sapling density was highest on the stumps; 75% of saplings were distributed on stumps, which occupied only 3% of the projected forest area. The mortality of saplings during the 3-year study period did not differ significantly among the three microsites. Six properties of stumps were quantified: height, diameter at ground level, decay class, species taxon, type (i.e., cause of occurrence: logging or natural disturbance), and light conditions. Of these properties, the type of stump was the most influential. The density of saplings was significantly greater on stumps produced by logging than on naturally broken stumps. Taller stumps exhibited a significantly higher density of saplings; indeed, there were no saplings on stumps less than 60 cm in height. Saplings on stumps were concentrated in higher positions on the stump, and survivorship tended to be greater when their root location was higher. We concluded that the height of the microsite was an important factor for regeneration of C. japonica at this research site, where saplings were under competition with dense undergrowth vegetation. Most of the fallen logs were less than 60 cm in height and it appeared unlikely that they would function as regeneration sites for C. japonica.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".