The Status of Seagrass Health: Supporting Sustainable Small-Scale Fisheries in Misool Marine Protected Area, Raja Ampat, Indonesia
Bibliographic record
Abstract
Seagrass plays an important role in aquatic resources, such as to support the sustainable management of small-scale fisheries, ensuring the availability of seagrass stocks for generations of local communities to cultivate in a sustainable manner. The purpose of this study is to provide information on the seagrass health status to support sustainable small-scale fisheries in the South Misool Regional Waters Conservation Areas which is located within the Raja Ampat Marine Protected Area of West Papua. The research was conducted in January 2019 in the Yefgag, Yellu and Harapan Jaya island. A total of ten quadratic transects measuring 1x1 m were laid perpendicularly to the coastline adapted from the seagrass watch method to collect the seagrass data, i.e. the species and the frequency of seagrass found, the dominance and the percentage of seagrass cover. Additional data on fish species were collected by interviewing the local fishermen directly. The relationship between seagrass cover and the number of fish species was analyzed. Th results showed that there were eight species of seagrass found in three observation stations, i.e. Halophila ovalis, Halodule uninervis, Halodule pinifolia, Halophila minor, Syringodium isoetifolium, Cymodocea serrulata, Cymodocea rotundata and Enhalus acoroides. According to the standard criteria for the health status of seagrass beds, the three locations are classified as less rich/less healthy. It because the seagrass coverage was in the range of 30-59%. The relationship between the percentage of seagrass cover and the number of fish species resulted equation of Y = 15,923x + 0,3174 with R2 = 0,763. It means that the percentage of seagrass cover affects the abundance of fish species by 76,3% with the remaining being influenced by other variables, such as water quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".