Responsible Fishmeal Consumption and Alternatives in the Face of Climate Changes
Bibliographic record
Abstract
Aquaculture expanded around 8.6% per year during the period 1980–2012. It is the greatest growing food producing sector. The intensification of fish production from aquaculture has made its demand for fishmeal from small pelagic fishes as an increasingly important issue. Recognizing the vulnerability of small pelagic fishes to challenges of climate changes is serious. It will have consequent challenges in terms of ensuring economically, socially and environmentally responsible fishmeal production practices. The possibility of replacing fishmeal with nutritionally comparable feedstuffs would diminish stress on prices of feed inputs resulting from captured fisheries. Diverse types of alternative (plant, animal, fishery by-products and novel foods) protein sources have been experienced in a variety of aquaculture feeds. This review aims to appraise the different kinds of fishmeal alternatives and the most proper substituent in fish diets. The paper in hand proposed that some of the described fishmeal alternatives could leads to a considerable drop in small pelagic fishes utilization, but still they might be more cost-effective than fishmeal. Studies should take into account both economic and biological assessment of dietary protein sources as fishmeal substituents. On the other hand, the environmental impacts of such alternatives should be evaluated in order to guarantee sustainability of fish feed industry.
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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 | 0.001 |
| 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) | 0.003 | 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".