Applying the ecosystem services - EBM framework to sustainably manage Qatar's coral reefs and seagrass beds
Bibliographic record
Abstract
Given the current natural and anthropogenic threats facing Qatar's marine environment and the consequential expected decline in ecosystem services, this paper examines the potential application of the Ecosystem Services-EBM framework developed by Granek et al. (2010) to sustainably manage Qatar's coral reef and seagrass bed ecosystems. Using interviews with stakeholders and field-collected data from sixteen coral reef sites and 6 seagrass meadows as well as secondary data, the paper presents new knowledge regarding the status of these ecosystems and the benefits they provide that are most valued by stakeholders. The research identifies existing and missing ecological and socio-economic data, as well as the processes and management strategies required to implement the five-step framework within a Qatari context. Key goals for implementing EBM identified by stakeholders include: adoption of scientific planning and valuation of marine environment, contextualizing and drafting legislation, regulations and policies in support of EBM; monitoring and enforcement of laws; and, promotion of public awareness and engagement. The article concludes with recommendations for filling remaining data gaps and highlights opportunities available to Qatar to become a leader in implementing EBM. These include maximizing the increasing role that stakeholders can play in mitigating further decline of the country's coastal ecosystems and leveraging mega events planned in Qatar, such as FIFA World Cup 2022.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".