Contributions of OBIS and GOOS to CBD and BBNJ
Bibliographic record
Abstract
The Ocean Biodiversity Information System (OBIS) and the Global Ocean Observing System (GOOS) play crucial roles in supporting international agreements focused on protecting marine biodiversity. These systems provide essential data and infrastructure for monitoring, assessing, and managing ocean biodiversity, directly contributing to the objectives of both the Convention on Biological Diversity (CBD) Kunming-Montreal Global Biodiversity Framework (GBF) and the Agreement under the United Nations Convention on the Law of the Sea on the Conservation and Sustainable Use of Marine Biological Diversity of Areas Beyond National Jurisdiction (BBNJ).As the ocean component of a Global Biodiversity Observing System (GBiOS), both OBIS and GOOS are recognised by CBD COP16 for their role in supporting the GBF monitoring framework, enabling countries to track biodiversity and assess progress towards the GBF 2050 goals and 2030 targets of ocean, species, and ecosystem protection. OBIS is specifically included in the GBF framework to develop complementary indicators for Target 20 ("Strengthen Capacity-Building, Technology Transfer, and Scientific and Technical Cooperation for Biodiversity") and Target 21 ("Ensure That Knowledge Is Available and Accessible To Guide Biodiversity Action").The BBNJ Agreement emphasizes transparency and data sharing, especially through its Clearing-House Mechanism, to support the information requirements for environmental impact assessments (EIAs) prior to authorizing activities in areas beyond national jurisdiction (ABNJ). It also manages access and benefit sharing on Marine Genetic Resources (MGRs), as well as the development of area-based management tools, such as marine protected areas. GOOS, through the definition of its essential ocean variables can guide the observing community to ensure that concurrent and complimentary observations required for assessing change and the effects of impacts are collected in standardised and robust ways. OBIS, with its extensive database of marine life observations, can directly contribute to this mechanism by providing data essential for assessing potential biodiversity impacts, guiding the EIA process, identifying risks, and informing mitigation measures. Additionally, OBIS data supports the identification of areas that need protection and informs design of effective management strategies for these regions. In addition, when Implementing sample batch identifiers would allow OBIS to track and trace the use of MGRs.OBIS and GOOS are essential components in the global effort to conserve and sustainably use marine biodiversity. By providing open access to data, coordinating observations, and supporting key processes outlined in international agreements like the CBD GBF and the BBNJ, they empower countries and stakeholders to make informed decisions, monitor progress, and ultimately achieve the goals of these agreements.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".