StreamStats—A quarter century of delivering web-based geospatial and hydrologic information to the public, and lessons learned
Bibliographic record
Abstract
First posted March 13, 2024 For additional information, contact: National Coordinator, StreamStatsU.S. Geological Survey1728 Lampman Drive, Suite DBillings, MT 59102 StreamStats is a U.S. Geological Survey (USGS) web application that provides streamflow statistics, such as the 1-percent annual exceedance probability peak flow, the mean flow, and the 7-day, 10-year low flow, to the public through a map-based user interface. These statistics are used in many ways, such as in the design of roads, bridges, and other structures; in delineation of floodplains for land-use zoning and setting of insurance rates; for regulatory purposes, such as the permitting of wastewater discharges; and for hydrologic and climate change studies. StreamStats was first developed for Massachusetts and released in 2001. The application provided users with the ability to obtain streamflow statistics computed from data collected at USGS streamgages and to obtain estimates of streamflow statistics for user-selected ungaged sites. Massachusetts StreamStats used geographic information system software and digital mapping to compute drainage-basin characteristics, which were then used in statistical models to estimate streamflow statistics for the user-selected sites. The statistical models were in the form of equations that were developed through a process known as regression analysis. StreamStats was the first known web application with the ability to do interactive geoprocessing.The utility of Massachusetts StreamStats was instantly apparent, leading the USGS to develop a version of StreamStats that could be implemented nationally. USGS State offices normally were required to develop custom regression equations and prepare local digital mapping data needed for implementing StreamStats for their States. Funding needed to complete this work usually was provided through cooperative agreements between the USGS and State agencies. In 2004, Idaho became the first to be released in the national version of StreamStats. By 2023, 44 States were fully implemented and six were undergoing implementation.StreamStats has undergone many modifications over the years to keep up with changes to the underlying software and to add functionality. Customized functionality and separate linked StreamStats applications were developed for several States. Meeting the high demand for additions and improvements to StreamStats while also adhering to budgetary constraints has, at times, been challenging. The StreamStats development team has identified numerous additional improvements that could be made to provide better performance and more functionality. The lessons learned from the experience of building and operating StreamStats for nearly 25 years could be relevant to others interested in pursuing efforts of a similar scale.
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 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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".