Practitioner needs to adapt to Sea-Level Rise: Distilling information from global workshops
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.011 |
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 it