Downstream strategies of liquid smoke products as a preservative and smoke aroma in fishery products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it