Climatic factors affecting radial growth of Betula ermanii and Betula platypylla in Kamchatka
Bibliographic record
Abstract
Radial growth responses to climate were studied in two species of birch broadly distributed across Kamchatka Peninsula. Wood cores were obtained in different locations and environments, from upper to lower treelines, and from wet maritime sites at the Pacific to the subcontinental interior of the peninsula. Response functions were calculated using the four longest meteorological records (1920s–2000) in Kamchatka. In Betula ermanii Cham., the dominant species in mountains and maritime woodlands, ring width in high-elevation (500–600 m) trees increased with warm and less rainy June and July and decreased with rainy/snowy cool weather during the prior September and October. Radial growth in B. ermanii low-elevation trees increased with higher winter precipitation, suggesting that water from melting snow prevents water stress and possibly desiccation in low-elevation trees. In Betula platyphylla Sukaczev, a common taiga species in interior Kamchatka, low summer temperatures limited growth at its upper distributional limit (300–350 m) and in cool, wet sites dominated by Picea ajanensis Lindl. et Gord. On drier sites dominated by Larix cajanderii Mayr., growth was limited by warm April and dry June weather. Variable responses along elevation–continentality gradients reinforce the necessity of a site-dependent differentiation for the assessment of impacts of climate change on species performance and geographic range shifts.
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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 | 0.001 |
| 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".