Life Cycle Environmental Impacts of Three Products Derived from Wild-Caught Antarctic Krill (<i>Euphausia superba</i>)
Bibliographic record
Abstract
Concern has been voiced in recent years regarding the environmental implications of the Antarctic krill fishery. Attention has focused primarily on ecological concerns, whereas other environmental aspects, including potentially globally problematic emissions and material and energy demands, have not been examined in detail. Here we apply life cycle assessment to measure the contributions of krill meal, oil, and omega-3 capsules to global warming, ozone depletion, acidification, eutrophication, energy use, and biotic resource use. Supply chains of one krill fishing and processing company, Aker BioMarine of Norway, were assessed. Impacts of krill products were found to be driven primarily by the combustion of fossil fuels onboard the fishing vessel and a transport/resupply vessel. Approximately 190 L of fuel are burned per tonne of raw krill landed, markedly higher than fuel inputs to reduction fisheries targeting other species. In contrast, the biotic resource use associated with extracting krill is relatively low compared to that of other reduction fisheries. Results of this study provide insight into the broader environmental implications of the krill fishery, comparisons between products derived from krill and other species targeted for reduction, opportunities for improving the fishery's performance, and a baseline against which to measure future performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".