Seasonal changes in the occurrence of embolisms among broad-leaved trees in a temperate region
Bibliographic record
Abstract
Sap freeze–thaw events are a main determinant of the distribution of broad-leaved woody plants in cool regions but the effect in other climates remains unknown. We used cryoscanning electron microscopy to examine the differences in plant growth patterns based on seasonal variation in xylem water distribution in four broad-leaved species (one ring-porous and three diffuse-porous species) in a temperate region of Japan. Leaf fall was detected in November for the ring-porous species Maackia amurensis Rupr. et Maxim., although embolisms were detected in large earlywood vessels in January of the following year. The percentage of embolisms in latewood vessels varied significantly between years. By contrast, xylem embolisms in diffuse-porous species (deciduous and evergreen) were barely detectable during winter. In one evergreen species, embolisms and refilling were detected in some vessels during the growing season. Based on the variation in the number of freeze–thaw events among years in Asian monsoon forests, we infer that M. amurensis obtained no benefit from extending leaf phenology because of the occurrence of vessel embolisms in winter. On the other hand, the leaf phenologies of deciduous and evergreen diffuse-porous species were less constrained by winter embolisms. Maackia amurensis persists in cool temperate regions by limiting the photosynthetic period.
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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".