Information/Data Needs for Floodplain Management
Bibliographic record
Abstract
Information is the principal resource of almost any government agency, and that is particularly true with agencies involved in floodplain management. Problems caused by the inaccessibility, incompleteness, inaccuracy, or untimeliness of the information can severely limit flood management personnel in meeting their objectives in the most productive and effective manner. In the aftermath of the Red River of the North (RRN) flood of 1997, the governments of Canada and the United States agreed that steps should be taken to reduce the impact of future flooding. They asked the International Joint Commission to analyze the cause and effects of the 1997 flood and to recommend ways to reduce the impact of future floods, including a relevant information base for the basin. An International Red River Basin Task Force (Task Force) was formed to conduct this work. The Task Force recommendation to devise strategies to effectively integrate and manage disparate data and information types parallels the vision of the Global Disaster Information Network (GDIN). GDIN is a multi-agency initiative within the U.S. to promote cooperation between user and provider interests in support to disaster management. To reach that goal the Task Force has been working to devise a strategy to identify an information infrastructure for the area of interest, to promote this concept within the broadest possible stakeholder community, and to make recommendations for a sustainable virtual presence. In July 1998, the Task Force conducted a series of workshops in Minnesota, North Dakota, and Winnipeg, Manitoba. A broad spectrum of stakeholders clearly articulated unmet needs in information and data availability, management, and the need for sustaining information infrastructure to improve emergency response and floodplain management. The process for generating local input for the information needs assessment as well as the results will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".