Impact of Coral Recruitment on Ecosystem Sustainability in Sempu Island Nature Reserve, Indonesia
Bibliographic record
Abstract
Coral recruitment, which serves as a key factor in the regeneration of coral populations, is vital for the sustainability of coral ecosystems.The coral ecosystem in the waters around Sempu Island, known for its declining coral cover, relies on this recruitment process.This investigation examines the relationship between hard coral cover and coral recruitment.The percentage of coral cover and the number of coral recruits were analyzed using Pearson correlation analysis.Data were collected at five research stations with the Underwater Photo Transect method in December 2022, February 2023, and April 2023.The coral cover in the Sempu Island Nature Reserve has been deteriorating over three consecutive survey periods, with percentages recorded at 14.28%, 13.92%, and 12.57%.A total of 22 coral recruits were recorded, belonging to six identified genera: Seriatopora, Porites, Pocillopora, Goniastrea, Pavona, and Acropora.Coral recruits and hard coral cover were positively but weakly correlated (r = 0.1119).Stable substrates such as dead coral with algae (DCA), dead coral (DC), and rock (RK) may facilitate coral recruitment.Coral recruits in the Sempu Island Nature Reserve compete with macroalgae and older corals for space and resources.Given the low levels of coral cover and recruitment, sustainable management measures should be implemented to protect the Sempu Island Nature Reserve coral ecosystem.
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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".