Bibliographic record
Abstract
Accurate floodplain maps are the key to better floodplain management.The days of expensive and unwieldy floodplain mapping by hand using paper maps are long gone.However, much of the analysis in floodplain management studies is still performed using separate modeling and mapping programs, which is less efficient than an integrated modeling and mapping approach.Today, new technologies, such as geographical information systems (GIS), global positioning systems (GPS) and remote sensing are helping floodplain managers to create accurate and current floodplain maps, with improved efficiency and speed, and at a reasonable cost.Today, it is possible to create floodplain maps that are dynamically linked to hydrologic and hydraulic models.This linkage allows more efficient map updates if the hydrologic or hydraulic parameters are changed.This chapter describes the latest technology and applications for developing floodplain models and maps.Examples and case studies, including the Federal Emergency Management Agency (FEMA)'s map modernization (Map Mod) and new risk mapping, assessment and planning (Risk MAP) programs, are discussed to illustrate the advances in floodplain modeling and mapping applications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".