Valuation of Economic Utilization of Fish Processing Waste Patin (Pangasius Hypopthalmus) as an Added Value for Fish Processing Industry Players in the District Kampar, Riau
Bibliographic record
Abstract
At this time there is no productive enterprise specializing in the utilization of solid waste from the processing of catfish into industrial raw materials of food and fish feed. Chances are this is due to the unavailability of data and information adequate technical and economical. In order to meet the needs that the research was conducted. This study aimed to analyze the economic valuation of the utilization of industrial solid waste processing of catfish, the raw material of functional food and feed that meet the quality standards in accordance with the National Standards Indonnesia (SNI) as well as to determine on whether viable or not the establishment of the business of processing food and feed of waste solid results catfish processing household or industrial scale. This research was a laboratory scale experiments using technology that has undergone several modifications. The results showed that the technology is able to produce industrial raw materials in the form of Fish Protein Concentrate (KPI), fish oil, bone powder and pyloric caeca crude enzyme in accordance with the Indonesian National Standard. Judging from the environmental impact assessment and business analysis (Gross Benefit Cost Ratio = 1.15> 1), amounting to 2,607 Profitabilty Ratio> 1, and an IRR of 65.91> 18%, then the utilization of solid waste business establishment catfish processing household scale feasible.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".