Functional differentiation among 12 dipterocarp species under contrasting water availabilities in Northeast Thailand
Bibliographic record
Abstract
Species composition varies greatly dependent on water availability gradients. In Northeast Thailand, dry deciduous forests (DDF) and dry evergreen forests (DEF) show contrasting species composition due to differences in soil structure and moisture. Although plant traits (physiological and morphological characteristics) are known to be involved in species distributions, which traits underpin these distinct distributions (either dry DDF or less-dry DEF) remain unclear. Here, we examined the differentiation of 21 leaf and stem traits between DDF and DEF using 12 dipterocarp species. We found that DDF species showed higher water use efficiency and higher water storage capacity in the lamina and petiole, higher leaf nitrogen content, higher stomatal density, larger leaves, thicker mesophyll layers, and a higher rate of water loss under severe dehydration than DEF species. Leaf osmotic potential at full turgor, wood density, and wood water content were not significantly different between DDF and DEF. We also observed a negative relationship between the potential photosynthetic capacity and the water loss rate during severe dehydration across species. Our results suggest that the differences in leaf traits related to photosynthesis and dehydration avoidance among the tree species produce niche differences along the soil water availability in tropical dry forests.
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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".