Measuring and Reporting on Seagrass as an Essential Ocean Variable for Science and Management
Bibliographic record
Abstract
Abstract To effectively manage and protect ocean life and the people who depend on it, we need coordinated, comparable observations of ocean biodiversity. Seagrass cover and composition is an essential ocean variable (EOV) of the Global Ocean Observing System because seagrasses are the foundation of coastal ecosystems worldwide, and support diverse marine life and ecosystem services. We present guidelines for collecting and reporting seagrass data that fulfill specifications for the EOV, including three priority measurements to maximize compatibility among data sets: seagrass cover, species composition, and areal extent, with priority environmental variables for interpreting changes in status and condition. To promote interoperability, we present a standard format for seagrass EOV data and metadata. These guidelines will enable better monitoring and assessment of seagrass ecosystems, facilitate syntheses, inform the Kunming–Montreal Global Biodiversity Framework headline indicator “Extent of natural ecosystems,” and support evidence-based conservation and sustainable development.
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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 | 0.065 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".