Bibliographic record
Abstract
This thesis is part of a larger project for the Texas State Soil and Water Conservation Board’s water yield enhancement program. The project will provide recommendations for hydrologic monitoring in the Canadian, Guadalupe, and Little Wichita river basins to quantify water yield from upstream brush control. The specific objectives of this thesis included collection of stream flow and precipitation data, analysis for historical trends and correlation between precipitation data and stream flow data, and proposal of locations to install new monitoring devices. Within the Canadian River watershed in Texas there was just one continuous stream flow station. Presently, the flow data from the Amarillo gage and the reservoir storage, evaporation, and withdrawal data collected by the Canadian River Municipal Water Authority provided all the available surface water information. Four precipitation stations existed in or near the watershed, but their data were variable and not significantly correlated. There was no significant correlation between the precipitation and stream flow data. At the Guadalupe River watershed, only two stations had periods of record long enough to provide useful data and their stream flow data were strongly correlated. However, these main channel stream flows had large variations, and the treated sites are along smaller tributaries. The nearest existing precipitation stations were downstream of the watershed. The precipitation data between the current stations showed a lot of similarities and they were strongly correlated. The Little Wichita River watershed was small with no stream flow station. The existing stream flow data at two downstream stations and the storage at Lake Kickapoo were extremely variable. There was only one nearby precipitation station and it was not in the watershed. Based on the evaluation of the existing monitoring devices in and near the watersheds, specific recommendations were made. Additional sites and costs for USGS stream flow gages were determined. Final locations for additional rain gages and weather stations will be set by the research team.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".