On the pre-adaptation of <i>Mitragyna</i> species to urban environments of Thailand
Bibliographic record
Abstract
As trees growing in urban environments are subjected to various stresses, it is important to select species that are naturally tolerant to such stresses. We compared the adaptability of three native Mitragyna species (M. diversifolia, M. hirsuta, and M. rotundifolia) from various parts of Thailand, growing in a wide range of environments and stresses. Various leaf morphological and physiological traits of trees growing along the roadside with their counterparts in the natural habitat were compared. The adaptability potential of the species, quantified through the plasticity index (PI), indicated an overall low plasticity for all traits. We attribute the low observed plasticity to the three species having a wide distribution range spanning diverse abiotic conditions. This includes low resource environments, and as such, they are pre-adapted to conditions similar to their native habitat. As the Mitragyna species already grow in diverse abiotic conditions, their ecological performance under stressful conditions (roadside) is not vastly different, potentially making them viable for planting in urban environments. We conclude that such species with similar trait level of PIs in both native and urban environments can successfully establish and thrive in urban green spaces under highly stressful urban conditions and provide sustained valuable ecosystem services.
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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) | 0.001 | 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".