Hubungan Kerapatan Lamun dan Kelimpahan Teripang (Holothuroidea) di Pulau Meosmangguandi Taman Wisata Perairan Padaido-Biak
Bibliographic record
Abstract
Seagrasses and sea cucumbers have a close relationship in determining ecological functions. The purpose of this study was to investigate the relationship between seagrass density and sea cucumber abundance on Meosmangguandi Island, Biak. The research was conducted in August-September 2021 at two stations. The location was chosen purposively on the East side (Station I, conservation location) and the West side (Station II, around residential areas and fishing activities). The 5m line transect method (2.5m to the right and 2.5m to the left) is used to reach the destination. The study found 6 species of seagrass namely C. rotundata, T. hemprichii, E. acoroides, H. pinifolia, H. ovalis and Syringodium isoetifolium, and 9 species of sea cucumbers namely Actinopyga lecanora, A. miliaris, A. mauritiana, Bohadschia similis, Holothuria atra, H. coluber, H. scabra, H. albiventer, and teripang malam. Percentage of seagrass cover at Station I was 85.51% and Station II was 57.71%, with a relative density of seagrass C. rotundata 39.10% and T. hemprichii 27.09%. The relationship between the density of seagrass C. rotundata and the abundance of sea cucumbers A. lecanora 4.285 > X2 (1:0.05); T. hemprichii with A. mauritiana 6.887> X2 (1;0.05); seagrass C. rotundata with A. miliaris 4.285 > X2 (1:0.05); A. miliaris with seagrass H. pinifolia 7.048 > X2 (1:0.05); density of seagrass T. hemprichii with abundance of sea cucumber B. similis: 5.274 > X2 (1:0.05); H. atra 5 > X2 (1:0.05); and H. coluber 5.172 > X2 (1:0.05), indicating an association between seagrass density and sea cucumber abundance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".