Seagrass biomass and sediment carbon in conserved and disturbed seascape
Bibliographic record
Abstract
Abstract Despite the Philippines having one of the widest extents and most diverse seagrasses, there are limited reports on the contribution of seagrass vegetation health to organic carbon (OC). Comparative assessments of OC between conserved and disturbed seagrass meadows in a seascape are also lacking. Conservation programs (e.g., marine protected area [MPA]) contribute to the maintenance of ecosystem health and OC storage in seagrass. However, disturbances may negate the effects of MPAs. Disturbances are often due to coastal development pressures linked to the need for industry, food production, and human settlement. Here, we assessed and compared the aboveground biomass (AGB), belowground biomass (BGB), and OC between conserved and disturbed seagrass meadows, then, tested its correlation with vegetation and water quality variables. The study was conducted in Oyon Bay (northwest Philippines), one of the key seascape MPAs in the Philippines. The bay experienced disturbances from a coal power plant, aquaculture, and human settlements for approximately 30 years. Results showed 7× higher AGB, 11× higher BGB, and 1.7× higher OC in conserved sites. The low biomass and OC values in the disturbed sites were correlated to poor vegetation and water quality conditions (particularly high turbidity [ ρ = 0.38], high phosphates [ ρ = −0.19], and low dissolved oxygen [ ρ = −0.31]). Our study showed the effectiveness of MPAs in maintaining ecosystem health and OC in seagrass, but the magnitude of disturbances lessened the benefits from MPAs. Our results have implications on the over‐ or under‐estimation of carbon sequestration not just in Philippine seagrasses but also in most Southeast Asian countries facing similar coastal development pressures.
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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 | 0.001 | 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".