The role of habitat creation in coral reef conservation: a case study from Aceh, Indonesia
Bibliographic record
Abstract
Abstract We describe the successful creation of new reef habitat on Pulau Weh, Indonesia. Coral cover on artificial reef modules increased from a mean of 24±SE 2.4% 1 year after the initial attachment of Acropora spp. coral fragments to 64±SE 4.8% after 3 years. The artificial reef modules were also rapidly colonized by coral recruits. Recruit densities were 53±SE 3.2 m−2 on modules that had been submerged for only 1 year, nearly twice as high as recruit densities on natural reef substratum (31±2.8 m−2). Consequently, the original Acropora assemblage had increased to include at least 23 coral taxa, including 10 additional Acropora species. The artificial reefs also supported at least 29 reef fish species, from 11 families. Unfortunately, this initial success in habitat creation was abruptly halted by a rapid rise in sea temperature in May 2010 that killed almost all corals on the artificial reefs and on nearby natural reefs. Notwithstanding the general view that reef rehabilitation is yet to deliver ecological and conservation benefits at meaningful scales, other benefits of this project included raising the awareness of reef conservation in the local community, promotion of tourism on Pulau Weh and job creation. We conclude, therefore, that habitat creation has a legitimate role as part of an integrated marine conservation strategy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".