Assessing the carbon sink capacity of coastal mariculture shellfish resources in China from 1981–2020
Bibliographic record
Abstract
The ocean has considerable potential to function as a carbon sink, absorbing anthropogenic CO 2 and buffering the effects of climate change. How the culture of shellfish can be used to increase the ocean carbon sink warrants evaluation. We analyze the production and carbon sink capacity of six important mariculture shellfish species (oyster, ark clam, mussel, scallop, clam, and razor clam) in nine coastal provinces of China from 1981–2020 using quality assessment and logarithmic mean Divisia index (LMDI) decomposition methods. Over this time period both cultured shellfish production and its contribution to the carbon sink generally increase, averaging approximately 600,000 t annually over the last four decades. Both the annual production and carbon sink capacity of China’s mariculture shellfish industry vary geographically. The total annual tonnage (scale) of cultured shellfish, and to a lesser extent, coastal shellfish species composition (structure) influence carbon sink capacity, and affect China’s plans to achieve “dual carbon goals.” Combining historical analysis and the LMDI method, we propose a scheme that optimally and more sustainably manages China’s culture of shellfish.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".