A Systematic Review on the Ecological Efficiency of Artificial Reefs for Lobster Fisheries in Malaysia
Bibliographic record
Abstract
Artificial reefs represent human-made constructs designed to emulate natural reefs, offering diverse habitats for marine species.Among other benefits, these reefs influence lobster appraisal and breeding, with artificial reefs (ARs) being crucial variables.Therefore, the objective of this study is to review the research on lobsters in Malaysia, specifically focusing on the ecological efficiency aspects as shown by existing academic resources.The entire study used systematic literature review (SLR).This systematic investigation used WoS and Scopus with PRISMA criteria.Employing carefully selected keywords, a total of 17 pertinent papers were identified.Due to a lack of research in Malaysia, this study includes papers from other countries.The findings have been categorized into two main sections: general findings and discussions centered on research questions and themes.These thematic discussions revolve around two primary themes, encompassing fisheries and marine biodiversity.This research evaluation shows that artificial reefs have a significant impact on the assessment of lobster value from several viewpoints.All the publications show that Malaysia has a gap in this field of study.Malaysia was represented in only one of 17 reviewed publications.Lobster valuation research with artificial reefs is rare.Given the rarity of such investigations, a complete examination helps identify shortcomings.These studies focus on artificial reefs' significance, needs, valuation, and function.The essential role lobsters perform is rarely assessed.This study could inspire future research on lobsters, artificial reefs, and their economic values.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".