Shared socio-economic pathways extended for the Baltic Sea: exploring long-term environmental problems
Bibliographic record
Abstract
Long-term scenario analyses can be powerful tools to explore plausible futures of human development under changing environmental, social, and economic conditions and to evaluate implications of different approaches to reduce pollution and resource overuse. Vulnerable ecosystems like the Baltic Sea in North-Eastern Europe tend to be under pressure from multiple, interacting anthropogenic drivers both related to the local scale (e.g. land use change) and the global scale (e.g. climate change). There is currently a lack of scenarios supporting policy-making that systematically explore how global and regional developments could concurrently impact the Baltic Sea region. Here, we present five narratives for future development in the Baltic Sea region, consistent with the global Shared Socioeconomic Pathways (SSPs) developed for climate research. We focus on agriculture, wastewater treatment, fisheries, shipping, and atmospheric deposition, which all represent major pressures on the Baltic Sea. While we find strong links between the global pathways and regional pressures, we also conclude that each pathway may very well be the host of different sectoral developments, which in turn may have different impacts on the ecosystem state. The extended SSP narratives for the Baltic Sea region are intended as a description of sectoral developments at regional scale that enable detailed scenario analysis and discussions across different sectors and disciplines, but within a common context. In addition, the extended SSPs can readily be combined with climate pathways for integrated scenario analysis of regional environmental problems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".