Some physiological properties of <i>Cryptomeria japonica</i> leaves from Kanto, Japan: potential factors causing tree decline
Bibliographic record
Abstract
Japanese cedar (Cryptomeria japonica D. Don) has been declining in urban areas of Japan. We examined if the decline was associated with physiological deterioration of leaves and resulting water stress. Leaves from three locations (severe decline, slight decline, and healthy) were analyzed for minimum transpiration rates (MT), amounts of epicuticular wax (EW), contact angles (CA), fractions of unhealthy stomata (US), cuticular thickness, and leaching of elements (LE). Anthropogenic elements (e.g., antimony (Sb)) in aerosols on the leaves were also analyzed by neutron activation analysis. MT, US, and amounts of Sb were 2, 15, and 10 times greater, respectively, at the severe decline location compared with the healthy location. LE was also greater at the severe decline location than at the slight decline and healthy locations. In contrast, CA was greatest at the healthy location and least at the severe decline location. MT correlated with the values obtained from a linear trinomial function that included EW, CA, and US as variables (r = 0.872, P < 0.01), and US correlated with amounts of Sb in aerosols (r = 0.939, P < 0.01). Therefore, it is likely that the deterioration of epicuticular wax and stomatal unhealthiness resulting mainly from clogging with aerosols, in combination with environmental aridification, have placed C. japonica under chronic and sometimes fatal water stress, causing tree decline.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".