Marine Ecosystem Restoring by High Complexity Artificial Reefs (HCAR)
Bibliographic record
Abstract
The establishment of a High-Complexity Artificial Reef (HCAR) along the Catalan coast in Spain prompted an investigation into the ecological rehabilitation of coastal ecosystems in the Western Mediterranean region. This study monitored marine succession by examining fish assemblage descriptors across seasons. Employing scuba diver video image analysis, we documented the emergence and evolution of HCAR structures from October to July. This analysis facilitated species identification, fish abundance quantification, and the assessment of the Shannon-Weaver Diversity Index at 5-second video intervals. The observed species primarily belonged to characteristic taxa of the western Mediterranean, with Pomadacys incisus (45.7%), Cromis chromis (26.9%), and Diplodus vulgaris (18.8%) among the frequently encountered species. Both fish abundance and the Shannon-Weaver Diversity Index exhibited an increasing trend over time, suggesting progressive ecosystem succession, notably during the spring-summer period. These findings highlight the potential of novel artificial reef designs to foster fish population growth and enhance biodiversity. However, to comprehensively assess the long-term stability and potential of HCAR, extended monitoring periods are imperative. In conclusion, this study underscores the positive influence of high-complexity artificial reefs on marine succession. It emphasizes the necessity for prolonged monitoring to elucidate their sustained impact on coastal ecosystems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".