Comparison of Manual Baseflow Separation Techniques to a Computer Baseflow Separation Program and Application to Six Drainage Basins
Bibliographic record
Abstract
The results from a computer baseflow separation program are compared to manual baseflow calculations in six drainage basins. The basins range in size from 19.5 to 287 square miles, are located from Oklahoma to New York, and are characterized by perennial streams. They were chosen to represent differences in drainage area, climate, and geology. Each of the basins, except the one in Oklahoma, have been the subject of baseflow calculations by previous investigators. The author estimated baseflow to the Little Washita River Watershed in February 1984 with seepage measurements. Estimates of baseflow by the computer program and the manual methods compare favorably. The fixed interval technique is generally not more th~n 20 percent greater than or less than baseflow calculated by ground-water rating curves, baseflow recession curves, and seepage measurements. The program has many advantages: readily accessible data base, it requires only mean daily stream discharge and basin area, rapid results, the calculations are reproducible, and the program may be run on a variety of microcomputers. Many previous baseflow studies utilized only one or two years of data or estimates of baseflow from nearby basins. Another purpose of this report is to show the amount of annual variation in baseflow. Ten consecutive years of rainfall and stream flow were analyzed for each basin, except one basin in Illinois which had a seven year data base. It was found that although baseflow as a percent of total runoff does not vary significantly, baseflow expressed as a percent of rainfall or as inches over the drainage basin can change by more than an order of magnitude from year to year. Therefore, baseflow depends upon fluctuations in rainfall, and cannot be expressed as a constant percentage or number of inches annually
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".