Uncovering the Brittle Star’s Genetic Diversity from Kalimantan and Bali
Bibliographic record
Abstract
Abstract Brittle star is a benthic organism that belongs to Echinodermata and plays an important role as a detritivore that balances the food chain in coral reef ecosystems. Several factors, including anthropogenic pressures, climate change, over-exploitation, and pollution, are known to threaten the brittle star’s biodiversity. Therefore, species identification research using molecular methods is essential. Molecular analysis can be conducted using the Cytochrome Oxidase I marker of mitochondrial genome DNA (mtDNA). The sequencing results will be compared with NCBI data to find the closest species. 5 of the total 8 samples were successfully sequenced, identifying three species: Ophiocoma schoenleinii, Breviturma pusilla, Ophiactis savignyi, and Ophiuroidea sp. The comparison of sequencing results with existing NCBI data yielded genetic distances ranging from 0.000 to 0.129. The genetic distance among clades ranged from 0.010 to 0.355. A phylogenetic tree was constructed to examine the relationships between our findings and brittle star data from various countries. We included data from 11 countries: Papua New Guinea, Australia, the United States, New Zealand, Canada, the West Indies, South Africa, South Korea, Brazil, Belgium, and France. Based on the distribution map, the research results are most closely related to data from Papua New Guinea.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".