Science Needs for Sea-Level Adaptation Planning: Comparisons among Three U.S. Atlantic Coastal Regions
Bibliographic record
Abstract
To identify priority information needs for sea-level rise planning, we conducted workshops in Florida, North Carolina, and Massachusetts in the summer of 2012. Attendees represented professionals from five stakeholder groups: federal and state governments, local governments, universities, businesses, and nongovernmental organizations. Over 100 people attended and 96 participated in breakout groups. Text analysis was used to organize and extract most frequently occurring content from 16 total breakout groups. The most frequent key words/phrases were identified among priority topics within five themes: analytic tools, communications, land use, ecosystem management, and economics. Diverse technical and communication tools were identified to help effectively plan for change. In many communities, planning has not formally begun. Attendees sought advanced prediction tools yet simple messaging for decision-makers facing politically challenging planning questions. High frequency key words/phrases involved fine spatial scales and temporal scales of less than 50 years. Many needs involved communications and the phrase “simple messaging” appeared with the highest frequency. There was some evidence of geographic variation among regions. North Carolina breakout groups had a higher frequency of key words/phrases involving land use. The results reflect challenges and tractable opportunities for planning beyond current, geophysically brief, time scales (e.g., election cycles and mortgage periods).
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".