A Cartographic Framework for Visualizing Risk
Bibliographic record
Abstract
Increased attention to global climate change in recent years has resulted in a wide array of maps and geovisualizations that forecast various scenarios. Since many consequences of climate change are inherently geographic in nature, effective cartographic representations that depict these risks are valuable for planning and mitigation purposes. In particular, sea-level rise resulting from climate change calls attention to the numerous representation issues that warrant consideration for hazard and risk mapping in general, including categorizing and representing risk, selecting an appropriate level of realism, and displaying potential impacts of a hazard on human populations as well as on the natural and built environments. Using examples of potential inundation from sea-level rise at global, regional, and local scales, the authors propose a conceptual framework of key cartographic considerations for maps, Web-based mashups, and geovisualizations that depict risk. The cartographic framework presented here may be extended to other risks of an ambiguous or fuzzy nature and may be used to organize key future research areas for hazard or risk mapping in general.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".