Enhancing Fisheries Management and Sustainability: A Stowage Factor Analysis of Fish Species at Mayangan Port, Indonesia
Bibliographic record
Abstract
The Stowage Factor (SF) is a crucial parameter for determining the volume-to-weight ratio of fish in a ship's hold, ensuring efficient space utilization and accurate catch estimation.This study investigates the SF of 17 fish species landed at the Mayangan Port, Probolinggo, Indonesia, using a fishing hold model to optimize fish storage in sampling boxes.Fish were placed in sampling boxes, which were sized to match the fish sampled.SF was calculated as the ratio of fish weight in tons to box volume in cubic meters.Packing density and spatial arrangement were controlled to reflect typical storage practices.Measurements were conducted on fish from frozen storage rooms with refrigeration to ensure consistency and accurate weights.The SF values measured across species ranged from 0.28 to 0.66 ton/m³, with an average SF value of 0.47 ton/m³.Significant variations were observed, influenced by species-specific morphology, packing density, and spatial arrangement.The results highlight that the average SF value, while useful as a general benchmark, may introduce bias when applied to species with SF values at the extremes of the range.These findings provide a scientific foundation for improving stowage design, emphasizing the importance of using species-specific SF values to support efficient logistics operations and data-driven fisheries management.Accurate SF measurements enable more precise estimates of fish weight in holds, contributing to the implementation of measured fisheries policies aimed at sustaining marine biodiversity and optimizing resource use.This research not only supports the design and management of fishing logistics but also aligns with ecological principles to promote the sustainable development of fisheries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".