Downstream strategies of liquid smoke products as a preservative and smoke aroma in fishery products
Bibliographic record
Abstract
Smoked fish is a fishery product that meets the nutritional needs of the population. The traditional smoking method leads to the production of H2S, which reduces the aroma and is carcinogenic. The liquid smoke technology offers a solution to the challenges associated with the application of traditional smoking methods. However, the use of the liquid smoke method remains limited in smoked fish businesses. This study aimed to evaluate and develop a downstream strategy for producing and distributing liquid smoke to facilitate its implementation by smoked fish businesses based on SWOT analysis. This study employed a quantitative descriptive methodology utilizing the strengths, weaknesses, opportunities, and threats (SWOT) analytical framework. The data were collected through interviews and questionnaires. The obtained data were subjected to weight calculations using the Expert Choice tool. The research findings indicate that the optimal approach for developing downstream liquid smoke products is to create a novel product in the form of liquid smoked fish. Liquid-smoked fish are immersed in or coated with liquid smoke to achieve an extended shelf life and smoky aroma, without traditional smoking methods. In addition, it establishes a strategic alliance between scholars, entrepreneurs, and the government. Strategic relationships can be established by developing a shared agenda focusing on fostering a sustainable blue economy. The blue economy refers to the use of hygienic, healthy, and non-carcinogenic fishing products such as smoked fish to promote sustainable economic growth and enhance community welfare.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".