Assessing potential driving factors of the ecosystem service value of mariculture shellfish in China using a structural equation modeling approach
Bibliographic record
Abstract
China is one of the major mariculture countries for shellfish in the world and provides more than 70% of the total global shellfish production. However, there is limited knowledge of the potential driving factors of the ecosystem service value of mariculture shellfish in China. Understanding what factors and how they drive the ecosystem can provide reference for further improving the ecosystem service value of mariculture shellfish, which is both theoretically and practically important for promoting the development of marine fishery economy in China. In this study, data of six major mariculture shellfish species in nine coastal provinces of China from 2009 to 2020 were analyzed using a structural equation modeling approach to quantify the effects of resource distribution characteristics and market demand on the ecosystem service value of mariculture shellfish in China. The results indicated that both resource distribution characteristics and market demand are important driving factors of the ecosystem service value of mariculture shellfish in China. Specifically, from the perspective of path coefficient, market demand plays a more important role (0.58) than resource distribution characteristics (0.36) in influencing the ecosystem service value of mariculture shellfish in China. Therefore, the research results for shellfish marine culture can be summarized as: (1) to actively respond to changes in shellfish market demand, (2) to enhance development planning for coastal shellfish cultivation area, and (3) to strengthen systematic management of mariculture shellfish resources. Results of this study could provide theoretical support and serve as a basis for promoting sustainable development of shellfish culturing industry and fisheries economy in China.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".