Population distribution and causes of mortality of smooth-coated otters, <i>Lutrogale perspicillata</i>, in Singapore
Bibliographic record
Abstract
) were transient in Singapore before one resident family group was observed in 1998, presumably having recolonized from Peninsular Malaysia. A population survey in 2017 revealed a minimum of 11 groups and 79 individuals. Since then, movements of otter groups within urban areas have led to increasing numbers of human-otter encounters, including conflicts. We determined the current abundance, population structure, and distribution of smooth-coated otters in Singapore. We assessed seven sampling zones nationwide through verified sighting records and social media. Mortality records from 2019 to 2021 were sourced from the Otter Working Group and Wildlife Reserves Singapore. In early 2021, there were a minimum of 17 groups and 170 individuals. Groups ranged from 2 to 24 individuals. Smooth-coated otters occupy coastal areas, waterways, reservoirs, and sites within the city center in urban gardens and ponds. Following territorial conflicts at waterways, smooth-coated otter groups moved into the urban matrix. Vehicle collisions are the main cause of mortality and are frequent at dams separating freshwater and coastal habitats. While there is a clear increase in smooth-coated otter numbers since 2017, there remain multiple natural and human-caused threats to otter persistence.
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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".