Low Greenhouse Gas Emissions from Oyster Aquaculture
Bibliographic record
Abstract
Production of animal protein is associated with high greenhouse gas (GHG) emissions. Globally, oyster aquaculture is increasing as a way to meet growing demands for protein, yet its associated GHG-emissions are largely unknown. We quantified oyster aquaculture GHG-emissions from the three main constituents of GHG-release associated with terrestrial livestock production: fermentation in the animal gut, manure management, and fodder production. We found that oysters release no methane (CH 4 ) and only negligible amounts of nitrous oxide (0.00012 ± 0.00004 μmol N 2 O gDW –1 hr –1 ) and carbon dioxide (3.556 ± 0.471 μmol CO 2 gDW –1 hr –1 ). Further, sediment fluxes of N 2 O and CH 4 were unchanged in the presence of oyster aquaculture, regardless of the length of time it had been in place. Sediment CO 2 release was slightly stimulated between 4 and 6 years of aquaculture presence and then returned to baseline levels but was not significantly different between aquaculture and a control site when all ages of culture were pooled. There is no GHG-release from oyster fodder production. Considering the main drivers of GHG-release in terrestrial livestock systems, oyster aquaculture has less than 0.5% of the GHG-cost of beef, small ruminants, pork, and poultry in terms of CO 2 -equivalents per kg protein, suggesting that shellfish aquaculture may provide a a low GHG alternative for future animal protein production compared to land based sources. We estimate that if 10% of the protein from beef consumption in the United States was replaced with protein from oysters, the GHG savings would be equivalent to 10.8 million fewer cars on the road.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".