THE CRUSTACEAN'S EXPORT-IMPORT MAPPING OF FISH QUARANTINE AND INSPECTION AGENCY (FQIA) JAKARTA I ON 2021-2022 PERIOD
Bibliographic record
Abstract
Indonesia's crustacean has great potential fisheries business. The depletion of fisheries natural resources needs to be solved to avoid overfishing, especially for crustaceans. The study aim was to analyze crustacean diversity and mapping its products on export and import markets, as well as to analyze the sustainability of crustacean export-import across Indonesia. The study was conducted at FQIA Jakarta I on January until February 2023. The descriptive analysis method was used. The results of the study showed that Indonesia's fisheries export were higher than import activities. On crustacean exports, the highest destination country for lobster (Panulirus sp.) is Cina, the highest destination country for crab (Portunus pelagicus) is the USA, the highest destination country for mud crab (Scylla serrata) is Cina, and the highest destination countries for mantis shrimp (Squilla mantis) are Hongkong and Cina. Meanwhile, for imported crustaceans, snow crab (Chionoecetes opilio) came from Japan, and American lobster (Homarus americanus) came from Canada and USA. The overfishing has not occurred as illustrated for fishing grounds in Indonesia. It indicated by the average of annual catch value which has not higher than MSY catch. Keywords: Crustacea, fish market, fishing ground, annual catch, sustainable.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".