The Capture Fishery Based Small Pelagic Business Development Opportunities Analysis with Purse Seine Fishing Gear in Maluku (Case Study on Financial Aspect in West Seram Regency)
Bibliographic record
Abstract
The study aims to find out and analyze the influence of running cost, shore cost, fishing duration and seasonal climate on profitability of purse seining small pelagic fishery business group in West Seram Regency in purpose to find out capture fishery development prospect by using such equipment in West Seram Regency. The data used in this study is primary data, i.e. a data collected from respondents through interview. Respondents are selected purposively from population of purse seining capture fishery business group. Multiple linear regression analysis method is applied to estimate the influence of running cost, shore cost, fishing duration and seasonal climate on the profitability variables (ROA). The findings reveal that running cost affects positively profitability, shore cost affects negatively and significantly profitability, fishing duration affects negatively and significantly profitability. On the contrary, seasonal climate does not affect profitability of purse seining capture fishery. Further, it is said that there is still a chance to develop purse seining small pelagic fishery business group under the condition that the activity has to be accompanied with control over running cost, shore cost, and fishing duration.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".