Bibliographic record
Abstract
Abstract The Arabian Gulf’s coastal and marine ecosystems are being negatively impacted by various factors such as population growth, coastal development, industrial and desalination plant discharge, and offshore oil and gas activities. However, seagrass meadows continue to show resilience and provide ecosystem values and services. This paper provides an overview of the seagrass meadows in the United Arab Emirates (UAE) in terms of their extent, species composition, threats, and conservation initiatives. The UAE’s coastline supports three seagrass species that are home to numerous marine species such as dugongs, green sea turtles, fish, and benthic invertebrates. With an area of around 2950 km2, subtidal seagrasses grow to a depth of 16 m and are one of the largest marine ecosystems in the Emirates. Seagrass beds also contribute significantly to blue carbon, with Abu Dhabi seagrasses estimated to have over 52 tonnes per hectare. The primary threats to seagrass meadows include dredging, landfill, and associated sedimentation, as well as environmental extremes such as high summer sea temperatures. However, conservation initiatives such as marine protected areas (MPAs) and federal laws have been implemented to protect these crucial coastal ecosystems.
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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) | 0.013 | 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".