Elucidating Environmental Determinants of Coral Species Richness at Pangandaran Beach: A Dissolved Oxygen-Centric Model
Bibliographic record
Abstract
Coral ecosystems are shaped by complex environmental gradients, and understanding these influences is critical for conservation efforts.This study presents a quantitative analysis of environmental factors contributing to coral species richness along Pangandaran Beach, situated on the southern coast of Java Island within the Indian Ocean-a region noted for its warm, clear waters conducive to coral proliferation.Utilizing line intercept transect surveys for coral assessment and Principal Component Analysis (PCA) for data interpretation, this research identifies a marked variation in species richness between the western (Pasir Putih) and eastern (Batu Numpang) sectors of the beach.The Pasir Putih area exhibits a robust positive correlation between coral species richness and environmental parameters such as dissolved oxygen (DO), light intensity, and water temperature.Conversely, Batu Numpang is characterized by lower species richness, which the Akaike information criterion model (AICc = -236.55)suggests is adversely affected by reduced DO levels-a stark contrast to the positive influence of DO in Pasir Putih (AICc = -14.06).These findings position DO as a pivotal environmental factor influencing coral diversity in Pangandaran Beach, with implications for targeted marine conservation strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".