Climate influences the effect of fungal decay type on regeneration of <i>Picea jezoensis</i> var. <i>hondoensis</i> seedlings on decaying logs
Bibliographic record
Abstract
Hondo spruce (Picea jezoensis var. hondoensis (Mayr) Rehder)) is separately distributed among several mountainous regions in central Japan as remnant populations of the last glacial period. To identify factors that affect Hondo spruce seedling regeneration on decaying logs, we investigated the relationships between climatic conditions, log properties, including decay type by fungi, and Hondo spruce seedling density on logs using data from seven subalpine Hondo spruce forests in central Japan. The results showed that the presence of soft rot was associated with higher seedling density, and the effect of brown rot in sapwood and white rot in heartwood on the predicted number of spruce seedlings on logs switched from positive to negative with increasing temperature and precipitation. Because soft rot occurs under humid conditions, the use of forest management techniques that increase the number of logs with soft rot in sapwood (e.g., by keeping the forest floor moist) are recommended for the sustainable regeneration of Hondo spruce. However, the relationships between wood decay type and seedling regeneration can also be affected by climate condition and thus are more complex than previously thought.
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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 | 0.000 | 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".