Advancing the climate-biodiversity-fisheries nexus in the UN Decade of Ocean Science for Sustainable Development
Bibliographic record
Abstract
Abstract Climate-driven changes in marine ecosystem structure and function adversely impact the biodiversity and sustainability of living marine resources, food security, and the resilience of coastal communities. Understanding how climate change impacts marine ecosystem biodiversity and global fisheries, i.e. the “climate-biodiversity-fisheries nexus”, is a fundamental element of the UN Decade of Ocean Science for Sustainable Development. Several Ocean Decade-endorsed Programmes within the climate-biodiversity-fisheries nexus are building global networks to transform our capacity to understand, forecast, manage, and adapt to climate-driven changes in ocean ecosystems, including sustaining blue food resources that provide essential food security and nutrition in a rapidly changing world. We compare the scope, objectives, global partnerships, and capacities of these Programmes, facilitating effective collaboration and identifying critical gaps in developing solutions to climate-driven changes in marine food webs, species assemblages, and global fisheries. This work complements the Ocean Decade Vision 2030 process by providing an assessment of actions that are underway and guidance to establish new actions needed to monitor and understand marine biodiversity and manage global fisheries within a changing climate. We provide recommendations for new and existing Ocean Decade Actions around the climate-biodiversity-fisheries nexus to help achieve the Ocean Decade outcomes of a “productive, predicted, healthy, and resilient ocean” by 2030.
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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 | 0.029 | 0.023 |
| 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 | 0.004 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".