Automated Lake Wide Flooding Predictions and Economic Damages on Lake Ontario
Bibliographic record
Abstract
The International Joint Commission (IJC) is presently re-evaluating the operational procedures for the Moses-Saunders Power Dam in Massena, New York, which controls the water levels of Lake Ontario and the flows of the St. Lawrence River. The weekly discharge rates at the dam range from 5,000 to 10,000 cubic meters per second and are regulated by a series of rules developed under the Boundary Waters Treaty of 1909 between the US and Canada. The current regulation plan is 1958D. New plans presently under consideration require complete impact evaluations for the system stakeholders that are sensitive to water level fluctuations, such as riparian property owners, the natural environment, and hydroelectric power generation. Baird & Associates was retained by the Buffalo District US ACE to evaluate the impacts of water levels for the alternative regulation plans under consideration on flooding and erosion hazards for riparian property. Refer to the companion paper for the discussion on erosion (Nairn and Zuzek). The study area included over 4,000 km of river and lake shoreline and 21,000 riparian properties. Given the vast geographic extent of the study area, complexity of the analysis, and fast-track schedule, the utilization of GIS technology and custom software applications was critical to the successful completion of the project. Baird relied on the functionality in the Flood and Erosion Prediction System (FEPS), which links GIS to engineering models and a relational database, to complete the analysis on budget and on schedule.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".