An integrated ecosystem approach for assessing the potential role of cultivated bivalve shells as part of the carbon trading system
Bibliographic record
Abstract
The role of bivalve mariculture in the CO2 cycle has been commonly evaluated as the balance between respiration, shell calcium carbonate sequestration and CO2 release during biogenic calcification. However, this approach neglects the ecosystem implications of cultivating bivalves at high densities, e.g. the impact on phytoplankton dynamics and benthic-pelagic coupling, which can significantly contribute to the CO2 cycle. Therefore, an ecosystem approach that accounts for the trophic interactions of bivalve aquaculture, including dissolved and particulate organic and inorganic carbon cycling, is needed to provide a rigorous assessment of the role of bivalve mariculture in the CO2 cycle. On the other hand, the discussion about the inclusion of shells of cultured bivalves into the carbon trading system should be framed within the context of ecosystem goods and services. Humans culture bivalves with the aim of producing food, not sequestering CO2 in their shells, therefore the main ecosystem good provided by bivalve aquaculture is meat production, and shells should be considered as by-products of this human activity. This reasoning provides justification for dividing up respired CO2 between meat and shell when constructing a specific bivalve CO2 budget for potential use of bivalve shells in the carbon trading system. Thus, an integrated ecosystem approach, as well as an understanding of the ecosystems goods and services of bivalve aquaculture, are 2 essential requisites for providing a reliable assessment of the role of bivalve shells in the CO2 cycle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".