An Estimate of Carbon Storage Capabilities from Wild and Cultured Shellfish in the Northwest Atlantic and Their Potential Inclusion in a Carbon Economy
Bibliographic record
Abstract
Bivalve mollusc production has been proposed as a method to reduce carbon emissions as (1) a low-emission protein source, and (2) via carbon stored in their shells. Numerous studies have examined the fate of carbon in bivalve production, estimated carbon sequestration rates, and their role in a carbon economy. To address the fate of shell carbon for the Northwest Atlantic, stored oceanic carbon equivalents (Eq), released CO2via calcification, and hypothetical carbon credit value ($24 tCO2), for both aquacultured and wild-captured bivalves for New England and Canadian Provinces on the Atlantic between 2016 and 2020 were estimated. Bivalve shells do not sequester atmospheric CO2, instead storing oceanic CO2 Eq and cannot be directly included in a carbon sequestration scheme. In the present study, total annual estimates of stored oceanic CO2 Eq were approximately 202,253 and 363,243 tons, with concurrent releases of approximately 121,255 and 217,771 tons of CO2 to the atmosphere, of which only 4% and 8% were from aquaculture production in Canada and the United States, respectively. Even if bivalve shells sequestered atmospheric CO2, current shellfish production levels are inconsequential with regard to current anthropogenic greenhouse gas (GHG) emissions. Stored oceanic carbon Eq for bivalve aquaculture is equivalent to 0.001% and 0.0005% of Canadian and US annual anthropogenic CO2 emissions, whereas wild-capture would store 0.028% and 0.005% of Canadian and US emission, respectively. Bivalve shell will not solve climate change, but the expansion of bivalve production provides a protein source with the lowest GHG emissions, which provides a multitude of environmental services.
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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 | 0.003 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".