Bibliographic record
Abstract
IV. EVALUATION OF FLOOD MANAGEMENT CASE STUDIES As local governments take on more responsibility for flood management, they will inevitably look to other local governments for successful models. This section considers three case studies which can provide guidance to other cities around the nation: (1) Fargo, North Dakota-Moorhead, Minnesota, (2) Cedar Rapids, Iowa, and (3) Sacramento, California. These regions make effective case studies because they are particularly vulnerable to flooding based on their topography and development history. More importantly, each city has advanced innovative flood management initiatives consistent with principles described in the EU Floods Directive. Fargo-Moorhead, Cedar Rapids, and Sacramento have taken steps that will help them manage floods in a more integrated manner, including the recognition of uncertainty in weather conditions and the need for better flood forecasting and the development of regional plans, and the use of some nonstructural solutions to reduce flood damage. Despite the advances in planning, however, implementation remains challenging. For instance, local governments have not consistently turned the language of integrated management into changes in land use ordinances. These local governments that have worked to develop more regional solutions have at times confronted obstacles relating to lack of coordination. These cases also demonstrate that the lack of federal requirements, substantial guidance, or consistent funding support continues to impede state and local governments from achieving optimal flood management planning. A. Background--Case Study Areas 1. Fargo, North Dakota-Moorhead, Minnesota The Red River of the North originates at the confluence of the Otter Tail and Bois de Sioux Rivers south of Fargo, North Dakota. It flows northward into Canada and forms most of the boundary between Minnesota and North Dakota. (157) The Red River's northward flow, distinctive in North America, contributes to more substantial spring floods because snow in the southern headwaters of the basin often melts before snow in the northern areas, leading to ice jams as the flow travels northward. (158) In addition, the Red River Basin is located within the broad, flat bottom valley of glacial Lake Agassiz. This topography causes the main stem and tributary rivers in the glacial lake plain area of the basin to overflow frequently onto broad floodplains. (159) The Red River Basin includes a large percentage of agricultural land, and the urban areas of Fargo, North Dakota and Moorhead, Minnesota. (160) These metropolitan areas have a combined total population of 200,000. (161) The Red River floods regularly. Flood damage has, on occasion, been catastrophic and has included severe structural damage to private and public facilities and infrastructure, extensive crop loss, major environmental degradation, and loss of life. Basin-wide flood damages (including both Canada and the U.S.) after the flood of 1997 were estimated at $5 billion. (162) Wetland destruction for farmland and climate change have increased the amount of precipitation and flooding in the region; the Red River has exceeded the National Weather Service flood stage of 18 feet in 48 of the past 109 years, and every year from 1993 through 2011. (163) The flood of record at Fargo-Moorhead was the 2009 spring flood with a stage of 40.8 feet on the Fargo gage. (164) Equivalent expected annual flood damages in the Fargo-Moorhead metropolitan area are estimated to be over $194.8 million in the future if no further action is taken. (165) 2. Cedar Rapids, Iowa Cedar Rapids, located in east-central Iowa, is the state's second largest city with a population of 125,850 and sits on both banks of the Cedar River. (166) It is located within a shallow bowl surrounded by gentle rolling slopes. Iowa's rolling prairies and hilly oak woodlands meet at Cedar Rapids. Upland water from the entire watershed flows into Cedar Rapids. …
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".