Incorporating New Datatypes to Enhance Species Delimitation: A Case Study in Rice Paddy Snakes (Homalopsidae: Hypsiscopus)
Bibliographic record
Abstract
Homalopsids (Old World Mud Snakes) include 59 semiaquatic species in Asia and Australasia that display an array of morphological adaptations, behaviors, and microhabitat preferences. These attributes make homalopsids an ideal model system for broader questions in evolutionary biology, but the diversity of this understudied group of snakes is still being described. Recognized species diversity in rice paddy snakes (Hypsiscopus) has recently doubled after nearly 200 years of taxonomic stability. However, the evolutionary distinctiveness of some populations remains in question. In this study, we compare mainland Southeast Asian populations of Hypsiscopus east and west of the Red River Basin in Vietnam, a known biogeographic barrier in Asia, using an iterative approach with molecular phylogenetic reconstruction, machine-learning morphological quantitative statistics, and ecological niche modeling. Our analyses show that populations west of the Red River Basin represent an independent evolutionary lineage that is distinct in genetics, morphospace, and habitat suitability, and so warrants species recognition. The holotype of H. wettsteini, a species originally described in error from Costa Rica, grouped morphometrically with the population at the Red River Basin and eastward, and those west of the Red River Basin are referred to the recently described H. murphyi. The two species may have diversified due to a variety of geological and environmental factors, and their recognition exemplifies the importance of multifaceted approaches in taxonomy for downstream biogeographic studies on speciation scenarios.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".