Techno-economic Analysis (TEA) of Extruded Aquafeeds
Bibliographic record
Abstract
The worldwide decline and overexploitation of ocean fisheries stocks had provided an incentive for the rapid growth of aquaculture. The aquaculture industry has been recognized as the fastest-growing food production system globally, with a 10% increase in production per year and is one of the most reliable and sustainable growth markets for manufactured feeds. Extrusion technology has been extensively used in the modern aquatic feed manufacturing, due to nutritional, physical properties improvements and cost effectiveness of feeds. Cost related to aquatic feed remains the biggest challenge, especially for small-scale producers. In order to understand costs and potential breakeven points, a single screw extruder and three different production scenarios (0.2, 2 and 20 t/day) throughput were used to develop techno-economic models for small-scale producers of extruded aquatic feeds. The results show annualized capital costs decreased as production capacity increased. Thus, aquatic feed producers could use this tool to evaluate annual costs and benefits to determine processing economics. Producers will have to consider the ingredients used, though, as raw ingredients constitute the greatest cost for the production of feeds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".