Anthropogenic Activities Alter The Seagrass Ecosystem In Southern Philippines
Bibliographic record
Abstract
Seagrasses are economically and ecologically important marine habitats. However, anthropogenic activities resulted in their decline globally. In the Philippines, MPAs were established, but most seagrasses need to be acknowledged and directly protected, thus affecting the ecosystem productivity. To prevent this scenario, baseline information that describes the status of seagrass beds is highly needed to help implement sound management practices. The present investigation was carried out to assess the effect of anthropogenic activities on the seagrass ecosystem in 15 municipalities as sampling areas across Southern Philippines. The study used focus group discussions, key informant interviews, and household interviews guided by structured questionnaires. Some 30 to 45 fishers and gleaners were interviewed in every municipality with 476 individuals. A matrix was developed for measuring anthropogenic activities complementary to random sampling of seagrass cover assessment. The anthropogenic activities considered to degrade the seagrass ecosystem and were analyzed in the matrix included tourism, gleaning/fishing, aquaculture, industrial and domestic activities. The result of the study showed that domestic, tourism and gleaning are the prevalent anthropogenic activities affecting seagrasses. The result further showed that higher anthropogenic activities affected lower seagrass percentage cover (R2=0.56). The result indicates that as anthropogenic activity increases, the cover condition of the seagrass ecosystem is averted. This implies that seagrasses should be acknowledged and included in the coastal management plans.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".