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 (CH4) and only negligible amounts of nitrous oxide (0.00012 ± 0.00004 μmol N2O gDW–1 hr–1) and carbon dioxide (3.556 ± 0.471 μmol CO2 gDW–1 hr–1). Further, sediment fluxes of N2O and CH4 were unchanged in the presence of oyster aquaculture, regardless of the length of time it had been in place. Sediment CO2 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 CO2-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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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; both teacher heads agree on what is shown here.
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".