Statistical Assessments of River Flow Alterations and Environmental Flow Standards
Bibliographic record
Abstract
A water availability modeling (WAM) system consisting of the Water Rights Analysis Package (WRAP) and input datasets for all Texas river basins has been used for statewide, regional, and operational planning and water allocation regulatory purposes for many years. The modeling system was recently expanded to support integration of environmental flow requirements in comprehensive water management. A strategy is presented in this paper for combining the WRAP-WAM modeling system with data management and statistical analysis tools to expand capabilities for analyzing stream flow alterations and the effects on flows of environmental flow standards. A Trinity River Basin case study demonstrates the utility of the modeling and analysis strategy in addressing relevant issues in the river systems of Texas and elsewhere. Dams and reservoirs constructed on the Trinity River and tributaries supply water for the Dallas–Fort Worth and Houston metropolitan areas, which are among the most rapidly growing large metro areas in the United States. Ecologically relevant statistical analyses of observed flows presented in this paper are designed to quantify flow alterations. Analyses of simulated flows representing natural and specified conditions of development are performed to assess the impacts of both water resources development and the establishment of environmental flow standards.
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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) | 0.000 | 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".