Capacity sharing to protect and restore ecosystems and biodiversity
Bibliographic record
Abstract
Abstract Challenge 2 of the UN Ocean Decade focuses on protecting and restoring marine ecosystems and biodiversity as a fundamental requirement to achieve sustainable development. Addressing this challenge requires reliable and timely information on biodiversity and ecosystems. To achieve this, academic, government, and private groups should engage in a process of co-design that aims to facilitate decision-making at the local and national level, and agree on common and interoperable practices for the collection and curation of biology and ecosystem information. Implementing the flow of data to enable the management of human activities and sustainable development will require the sharing of capacity. An all-hands-on-deck effort will help us ensure a better future for ourselves. A positive step would be to identify the minimum essential ocean variables that can serve multiple relevant regional and international frameworks and to link and harmonize the required data and information flow (i.e., for frameworks including the Convention on Biological Diversity Kunming-Montreal Global Biodiversity Framework, the United Nations Framework Convention on Climate Change Paris Agreement, the Biodiversity Beyond National Jurisdiction Agreement, the International Seabed Authority, the Convention on the Conservation of Antarctic Marine Living Resources, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, and deep and national ocean fisheries policies). A key strategy is to support and build on existing local and national networks for biodiversity observation. With this information, local communities and nations can better understand and manage how they use marine life and also report on progress toward Sustainable Development Goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".