Towards a global dataset of seagrass occurrences: current progress, knowledge gaps and challenges
Bibliographic record
Abstract
For a number of years, the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) has collaborated with Dr. Frederic Short (SeagrassNet, University of New Hampshire) and other seagrass experts worldwide to compile a global georeferenced dataset of seagrass occurrences. More than 184,000 point and polygon records have been collated to date. The GIS dataset and associated metadata can be downloaded from UNEP-WCMC’s Ocean Data Viewer, thereby providing ready-for-use information on the location of this critical habitat to policy-makers, conservationists, and scientists. This knowledge is necessary to inform better decisions regarding marine conservation (e.g., marine spatial planning) and to ensure the sustainable use of our ocean’s resources (e.g., ecosystem service valuation). This and other similar datasets on the distribution of key marine habitats—e.g., saltmarshes, mangroves, and corals (also curated and distributed by UNEP-WCMC)—have been used in numerous global and regional studies that examine the status of sensitive marine biodiversity and related impacts. Occurrence datasets such as these are also used to inform predictive models aimed at filling spatial gaps in knowledge. Moreover, such a global dataset can support analyses that explore the contribution of seagrass and other ‘blue carbon’ ecosystems (e.g. saltmarsh, mangrove) to carbon sequestration, thereby aiding climate change mitigation. As part of an interactive session, we will: 1. Present current progress towards collating a global dataset of seagrass occurrences, highlighting the achievements of such collaborative endeavours and the relevance of this dataset to global science, conservation, and policy initiatives; and 2. Facilitate a discussion with Mediterranean seagrass experts to develop recommendations for addressing the knowledge gaps and challenges (e.g. licensing issues) that have been identified. In particular, this session will focus on improving our collective knowledge of the spatial distribution of seagrass ecosystems in data-poor regions of the Mediterranean by drawing from local and regional expertise. Given the importance of these ecosystems to sustaining marine biodiversity, regulating carbon, and supporting global fisheries, such contributions towards developing a comprehensive and accurate dataset can help to ensure that scientists, conservationists, policy-makers and other decision-makers have the appropriate information to make better-informed analyses and decisions.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".