Semi-natural Spawning of the Hal Semi-natural Spawning of the Halmahera Walking Shark (Hemiscyllium halmahera) at UPTD Perikanan Unggulan Morotai
Bibliographic record
Abstract
This study aimed to examine the semi-natural spawning process of the Halmahera walking shark (Hemiscyllium halmahera), an Indonesian endemic species of high conservation value. The research was conducted at the UPTD Perikanan Unggulan Morotai from July to September 2025 using a field-based quantitative experimental approach. Four broodstock (three females and one male) collected from the wild were maintained in a 3,500-liter semi-natural tank designed to simulate natural habitats with coral and gravel substrates. Water quality parameters—temperature, pH, salinity, and ammonia—were measured every three days to identify their influence on reproductive activity. The findings revealed that temperature (r = 0.97; p < 0.01) and salinity (r = 0.88; p < 0.05) were the most influential environmental factors triggering spawning, while pH and ammonia had secondary roles. During the 90-day observation period, two spawning events occurred, producing two pairs of egg capsules with typical morphology, although no embryonic development was detected. The study demonstrates that a semi-natural system can effectively stimulate natural reproductive behavior in H. halmahera and provides a scientific foundation for hatchery-based conservation of endemic elasmobranch species in Indonesia. Future studies with extended observation periods and hormonal stimulation are recommended to enhance fertilization success.
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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".