Needle browning and death in <i>Pinus pumila</i> in the alpine region of central Japan were not related to mechanical damage of cuticle and cuticle thickness
Bibliographic record
Abstract
The browning and death of needles of evergreen conifers in alpine regions occur mainly in early spring at the point where the shoot protrudes from the snowpack. They are thought to be caused by excessive transpiration due to mechanical damage to the cuticle or to a thinner cuticle. However, there are a few studies that do not necessarily agree with this idea. We assessed needle browning and death in Pinus pumila (Pallas) Regel. in the alpine region of Japan in relation to mechanical damage to the cuticle, cuticular resistance, and cuticle thickness. Mechanical damage was not observed on needle cuticles of Pinus pumila browning in a natural environment. The color of needles with artificially abraded cuticles changed from green to brown in the abraded part. However, the brown color at the abraded part differed from the brown of a browning needle in its natural environment. There was no correlation between cuticular resistance and cuticle thickness. Needle browning and death in P. pumila were not related to mechanical damage of the cuticle or to cuticle thickness but might be due to changes in the quality and structure of the cuticle and other stresses.
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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) | 0.000 | 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".