Bibliographic record
Abstract
Abstract. The Greater Mekong Subregion (GMS) is one the global biodiversity hotspots. However, the diversity has been seriously threatened due to environmental degradation and deforestation, especially by expansion of rubber plantations. Yet, little is known about the impact of expansion of rubber plantations on regional plant diversity as well as the drivers for plant diversity of rubber plantations in this region. In this study, we analyzed plant diversity patterns of rubber plantations in the GMS based on a ground survey of a large number of samples. We found that diversity varied across countries due to varying agricultural intensities. Laos had the highest diversity, followed China, Myanmar, and Cambodia. Plant species richness of Laos was about 1.5 times that of Vietnam. We uncovered latitudinal gradients in plant diversity across these artificial forests of rubber plantations and these gradients caused by environmental variables such as temperature. Results of redundancy analysis (RDA), multiple regression, and random forest demonstrated that latitude and temperature were the two most important drivers for the composition and diversity of rubber plantations in the GMS. Meanwhile, we also found that higher dominance of some exotic species (such as Chromolaena odorata and Mimosa pudica) was associated with a loss of plant diversity within rubber plantations; however, not all exotic plants cause the loss of plant diversity in rubber plantations. In conclusion, not only environmental factors (temperature), but also exotic species were the main factors affecting plant diversity of these artificial stands. Much more effort should be made to balance agricultural production with conservation goals in this region, particularly to minimize the diversity loss in Vietnam and Cambodia.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.176 | 0.131 |
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".