THE IMPACT OF OVERFISHING OF BIGEYE TUNA (THUNNUS OBESUS) POPULATIONS ON THE BALANCE OF THE INDONESIAN MARINE ECOSYSTEM: A LITERATURE STUDY IN THE BANDA SEA
Bibliographic record
Abstract
Indonesia has a sea area of approximately 3.1 km2 including inland areas, archipelagic waters, territorial seas, the Exclusive Economic Zone. The length of Indonesia's coastline is estimated at around 54,716 km, making Indonesia the country with the second longest coastline after Canada. The length of this coastline reflects the diversity and richness of marine ecosystems in Indonesia and is rich in abundant fishery resources, one of which is bigeye tuna (Thunnus Obesus). The research method used in this article is a literature review which includes a comprehensive review of various scientific sources, research reports, and statistical data related to the impact of overfishing of bigeye tuna on the balance of the Indonesian marine ecosystem, especially in the Banda Sea. Overfishing practices have caused a decline in the population of this fish, with a significant impact on the balance of the marine ecosystem in the Banda Sea. Unsustainable fishing practices, the use of destructive fishing gear, and minimal supervision and law enforcement against illegal fishing practices have contributed to the decline in the population of bigeye tuna (Thunnus Obesus). The impacts of overfishing of bigeye tuna (Thunnus Obesus) are: Drastic Decrease in Tuna Population, Food Chain Imbalance, Impact on Other Species, Marine Habitat Damage, and Biodiversity Decline, thus becoming a national and global threat. To overcome the impacts of overfishing of bigeye tuna in the Banda Sea, it is necessary to set strict catch quotas, stricter supervision and law enforcement, use of environmentally friendly fishing gear, restoration of damaged marine habitats
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".