The "why and how" of marine functional connectivity for sustainable development
Bibliographic record
Abstract
Marine Functional Connectivity (MFC) is a dynamic ecosystem process encompassing all the flows (of individuals, genes, matter and energy) driven by changes in the distribution and movement of marine organisms at sea and across the land-sea-air interface. Continuing advances in MFC understanding enhance our ability to evaluate the status, trends, and variability in marine species abundance, the drivers of change, and the role of marine species movement in the functioning of the biosphere. Ultimately, good knowledge of MFC is fundamental to advance models and forecasts of species and ecosystem distributions and resilience, and to develop scenarios that are possible outcomes of policy decisions. This talk explores how MFC knowledge can best improve the design of effective strategies for the sustainable and equitable use of marine ecosystems and resources. It summarizes practical transdisciplinary findings from several expert groups, convened in 2024 by the European COST Action/UN-Ocean Decade project SEA-UNICORN, the O-CONNECT working group of the French OMER GDR, and the UN-Ocean Decade Programme Marine Life 2030 to initiate the co-design of “the connectivity science and decision-making tools we need for the ocean we want”. Bringing together over 80 scientists, policymakers, and stakeholders from 22 countries, this initiative led to the production of key recommendations on how best to advance MFC data production and use to enhance: (1) Marine Protected Area design and management; (2) fisheries management; (3) blue economy development; (4) integrated management at the land-sea interface; (5) coastal and marine restoration; and (6) deep seas and high seas exploration and management.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".