Optimizing Coral Farming: A Comparative Analysis of Nursery Designs for Acropora aspera, Acropora muricata, and Montipora digitata in Anantara Lagoon, Maldives
Bibliographic record
Abstract
Maldivian coral reefs have undergone a substantial degradation due to a combination of anthropogenic pressure and global climate change. In response to the 2016 coral bleaching event, Anantara Dhigu, Anantara Veli, and Naladhu Private Island launched the Holistic Approach to Reef Protection (HARP) project, aiming to restore the house-reef. This study, a key component of HARP, evaluates the effectiveness of two coral nursery designs, rope-based and metal table-based, at two depths (⁓2 m and⁓5 m) for the propagation of Acropora aspera , Acropora muricata , and Montipora digitata . Over six months, bimonthly underwater surveys assessed growth rates, ecological volume, health conditions, disease presence, predation, and survival rates. Results indicate that rope-based nurseries generally outperform metal tables, with higher growth rates and better survival rates observed on ropes. Specifically, Montipora digitata showed the most substantial growth at 2 meters depth, reaching up to 5 cm in six months. Additionally, rope nurseries demonstrated significantly lower predation rates and better overall health conditions compared to metal tables. While coral survival was comparable across nursery designs, the study suggests that shallower depths favor coral growth due to enhanced light availability. However, shallower depths also correlated with higher mortality during a recent bleaching event, emphasizing the need to balance depth considerations with potential thermal stress. This study underscores the advantages of rope-based nurseries in promoting coral growth and survival, offering valuable insights for coral restoration strategies in shallow lagoon ecosystems.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".