Global reduction fisheries and their products in the context of sustainable limits
Bibliographic record
Abstract
Abstract Globally, one‐sixth of landings from marine capture fisheries are destined for the production of fishmeal and fish oil ( FMFO ), which are currently overwhelming utilized by fed aquaculture. Many different species are used globally for the production of FMFO , but little concern has been given to the divergent environmental and ecological impacts of FMFO products based on species, ecosystem and fishing gear used in their capture. We evaluated the variable environmental performance of FMFO products from a wide range of fisheries whose products are either primarily intended for reduction or whose by‐products are redirected to reduction. Assessed fisheries accounted for 44% of global reduction fishery landings in 2014. Analysis was conducted on the basis of two measures: the carbon footprint (i.e., greenhouse gas [GHG] emissions) and the marine footprint (i.e., primary production required [PPR]). We found large differences between the impacts of FMFO products across the 18 reduction fisheries examined. Cumulatively, we estimate that reduction fisheries emitted 4.6 million tonnes of CO 2 ‐e GHGs in 2014, and appropriated over 4% of primary production in some ecosystems, demonstrating a non‐trivial impact. As demand for aquafeeds grow, the sustainable sourcing of raw material inputs will be of great importance. Results here suggest that the source of FMFO inputs can have dramatic effects on the environmental performance of fish feeds and fed aquaculture. We recommend further research on the environmental and ecological impacts of food production systems, and specifically to understand these results in relation to global production and sustainable boundaries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".