Economic Contribution in the Management of Solid Waste Policy Processing Water on Improvement of Fish Processing Revenues
Bibliographic record
Abstract
This study aims to analyze the economic contribution in the environmental management of solid waste processing of catfish to increase income of fish processing in Koto village Kampar regency Riau.The research method used is the method of exploitation of solid waste processing and case studies with respondents processing catfish (fillet fish, salai and salted fish). Data were collected using questionnaires on business actors, and solid waste processing trials (meat waste, fish head, fish bone, belly fat and edible offal or stomach). The waste is processed into raw material for food and feed industries that meet the quality standard of Indonesian National Standard (SNI) and calculated its economic value include business analysis (Gross Benefit Cost Ratio), Profitabilty Ratio, and IRR and environmental management scenario of catfish fish industry. The data obtained will be homogenized data and then tabulated and analyzed descriptively quantitative.The results showed that the net production technology capable of producing industrial raw materials in the form of fish meal (fish protein concentrate (KPI), fish oil, bone meal and fish skin chips with the Indonesian National Standard.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".