Practitioner needs to adapt to Sea-Level Rise: Distilling information from global workshops
Bibliographic record
Abstract
Climate-induced sea-level rise threatens the world’s coastal populations, critical infrastructure, and ecosystems. The science of sea-level rise (SLR) has developed to inform understanding of global climate mitigation and adaptation challenges, but there is much less engagement with practitioners to discern their climate services needs and support the development of adaptation planning and action on the ground. In addition, adaptation planning and implementation processes for SLR are relatively new and practitioners developing leading practices are seeking interaction with their peers and the SLR science community. To address these gaps, we co-produced online global workshops with sixty-nine practitioners from twenty-six countries. These workshops aimed to increase understanding of the state of SLR adaptation planning practice worldwide, gather information on practitioners' existing knowledge and service needs to advance their adaptation efforts, and facilitate exchange between practitioners engaged with coastal adaptation and the SLR science community. The workshops uncovered commonalities across contexts and identified consistent needs from scientists and other technical experts amongst the practitioner community. These needs include generating more localized SLR impact data, understanding of compound risk, creating data timelines for decision making, and developing clarity about uncertainties and probabilities. We also observed important differences between urban and rural locations and between places with different economic resources. To meet their needs, practitioners identified three crucial next steps: 1) Develop more online engagement opportunities, 2) Establish a global practitioner community of practice, and 3) Scale and improve the provision of climate services.
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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 | 0.061 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".