Connectivity, at sea and at the land-sea interface: unveiling the overlooked role of marine biodiversity in Biosphere functioning
Bibliographic record
Abstract
Organism movement plays a crucial role in the transfer of genes, matter, and energy between habitats, both in marine environments and across the land-sea interface. The numerous fluxes resulting from the lifetime and trans-generational displacements of all marine species (from bacteria to whales), collectively referred to as Marine Functional Connectivity (MFC), underpins planetary health and diverse ecosystem services. However, awareness and integration of this connectivity in marine research, management and policy is still limited. Given surging environmental change, resource overexploitation, habitat loss and fragmentation, and the global transport of non-native species, accurately estimating and predicting MFC patterns is vital to support global policy goals aimed at conserving and restoring ocean biodiversity and function. This talk provides an overview of the current state of MFC research and identifies key challenges and future directions for this emerging field, critical for supporting truly multidisciplinary marine science for improved management and policy. Placing MFC research at the heart of marine environmental science and management promises to increase ecological and socio-economic resilience worldwide, and improve the sustainable use of ecosystems and resources, at sea and at the land-sea interface.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".