Monsoon climatic signal is stronger in wood anatomical traits than in ring widths of Fokienia hodginsii in central Vietnam
Bibliographic record
Abstract
Dendrochronological studies conducted in tropical regions in recent decades revealed that some species exhibit annual rings. In Southeast Asia, several ring-width chronologies of Fokienia hodginsii (Dunn) A. Henry & H H.Thomas were established to reconstruct past climatic conditions. Nevertheless, despite the strong correlations with meteorological data, the climate signal in the ring-width chronologies is robust for the shoulder season of the monsoon. For those interested in intra-annual climate signals, other parameters might prove useful. Information on intra-annual variability is crucial for understanding tree growth processes and climate dynamics in tropical regions. In this study, we explore the potential of wood anatomical parameters for climate reconstruction and for gaining a better understanding of the current climate of Southeast Asia. Microslides from cores of F. hodginsii in central Vietnam were analyzed. Notably, average lumen perimeter and lumen length demonstrated the most robust climate signals. They exhibit stronger correlations than ring width with maximum temperature, precipitation, and the Standardized Precipitation Evapotranspiration Index. The highest correlation (r=0.5) was observed between lumen area and October-November-December precipitation. Furthermore, earlywood parameters show higher correlations with precipitation than whole ring parameters. The growth of earlywood in F. hodginsii appears to be influenced by climatic conditions during September-October-November of the previous year and April of the current year. Thus, earlywood is generally produced around April, whereas latewood grows during or after April-May-June. This study is the first attempt to use anatomical parameters to reconstruct climate in Southeast Asia and provides valuable insights into the intra-annual growth dynamics of tropical tree rings.
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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) | 0.001 | 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".