Supporting Global Blue Economy through Sustainable Molluscan Mariculture with a Focus on China
Bibliographic record
Abstract
Molluscan mariculture has become increasingly common in coastal areas of China with production accounting for ∼69% of Chinese total mariculture production. In other international locations, however, a possible underutilization of molluscan mariculture may result from hesitancy based on initial environmental impacts. This study investigated the dynamic relationship between the molluscan mariculture industry (MMI) development and environmental quality (solid waste production, total wastewater emissions, and sulfur dioxide emissions) at the meso level. Their relationship trend followed that described by the environmental Kuznets curve theory in ∼78% of the areas, and ∼89% of the areas were in a coupled coordination state in most periods. This implied that the healthy development of the MMI can reduce environmental stress. These results should help to alleviate the negative perceptions of some researchers and the public regarding mariculture operation. The findings suggest that China, given its substantial contribution to global blue growth, should further support sustainable molluscan production by developing new strategies and policies and reducing potential negative impacts of mariculture on the environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".