65 Sustainability of fish-derived feed inputs: an ecological perspective
Bibliographic record
Abstract
Abstract Fishmeals and fish oils (FMFOs) sourced traditionally from directed fisheries and increasingly from seafood processing by-product streams, are important sources of key nutrients in animal diets. While attention is understandably paid to the relative nutritional attributes and prices of FMFOs, little consideration is typically paid to the resource utilization and environmental performance attributes of these products, beyond attending to the management status of source fisheries. This is somewhat surprising, however, given the diversity of species and ecosystems from which FMFOs are derived, their relative abundance, the technologies used in their capture, and energy sources used in their processing. Here, we present results of analyses of the greenhouse gas emission intensity (carbon footprint) and area of marine ecosystem to support the primary productivity required to sustain production of the species-specific biomass required (marine ecological footprint) to yield a tonne of fishmeal or oil for 18 distinct combinations of species, source ecosystem and fishing gear used to produce fishmeal and oil. Analyses of the carbon footprint of meals and oils included direct combustion and related upstream GHG emissions associated with direct fuel inputs to fishing along with emissions from processing-related fuel and electricity inputs. Analyses of the marine ecological footprint of meals and oils accounted for the trophic level of the source species as well as the source ecosystem-specific trophic transfer efficiency and average annual primary productivity. Results indicate that the carbon footprint of FMFOs varied by an order of magnitude while the marine ecological footprint of meals and oils varied by four orders of magnitude amongst the sources assessed. Given these substantial environmental and ecological performance differences between many widely traded FMFOs, there is substantial scope for feed formulators to construct and market low environmental impact feeds while maintaining nutritional and cost profiles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".