Bathymetry and Sediment Accumulation of Walker Lake, PA Using Two GPR Antennas in a New Integrated Method
Bibliographic record
Abstract
Abstract Silting within all man-made reservoirs can be a major problem because of a lower potential water storage. Exploring a lake’s bathymetry with electromagnetic techniques is one way to identify the magnitude of sediment accumulation in these reservoirs. In this study, the bathymetry and sediment accumulation of Walker Lake, Pennsylvaia were explored with ground penetrating radar (GPR) using either a 400 or 100 MHz antenna, depending on the depth of the lake. The assembled apparatus herein included two GPR antennas placed in an inflatable boat towed by another boat powered by an electrical trolling motor. A total of eighteen crossings were performed along the entire length of the lake and a new integrated method using multiple processing software was applied to generate three-dimensional and contoured surfaces of bathymetry, sediment accumulation, and the original 1971 basin topography prior to the construction of Walker Lake Dam. The bathymetry, volume of sediment, and its accumulation rate were estimated. The lake depth was found to vary between a few centimeters near the inlet to 9 m nearer the dam. Deposition of sediment takes place mainly near the inlet to the lake and along the old channel of Middle Creek. The sedimentation gradually decreases toward the dam, ranging between 0 and 1.85 m in terms of bulk sediment volume.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".