Climate Assessment Moves Local
Bibliographic record
Abstract
Abstract State and local governments, businesses, community organizations, and the general public are taking an increasingly significant role in climate impact assessment. Driven by impacts to constituents and customers and threats to human health, essential services, and property values, local organizations are on the front lines of responding to climate change. National and international efforts such as the U.S. National Climate Assessment and Intergovernmental Panel on Climate Change provide fundamental scientific understanding as well as methods and modeling tools. Subnational climate assessments can build on this foundation and tailor models and analyses to specific local or decision contexts. However, subnational climate assessment and adaptation presents new scientific and research challenges, such as those related to downscaling climate models, simulating extreme events, and understanding local values and institutional practices. As state, local, and sectoral assessments become more common across the nation, there is a critical opportunity to share learnings and identify challenges and pitfalls. Building off a National Academies of Sciences, Engineering, and Medicine activity focusing exclusively on subnational climate assessment, we consider the methods and results from a variety of examples to synthesize findings about current practices and the future of subnational climate assessment.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".