Seismic determination of bedload and morphodynamic trends in the Nisqually River of Mount Rainier National Park, WA, USA
Bibliographic record
Abstract
We are hoping to explore non-contact hydrology techniques, and have landed on the use of seismic signals as one of our data sources. My hope is to use one buried unit full-time for the next three years, and an array of temporary nodes during the warmer months where the ground is more accessible. The buried unit would be located just above the trimline of the Nisqually River near Longmire WA, and the nodes would be distributed in strategic locations proximal to the Nisqually River within the boundaries of Mount Rainier National Park. The location of nodes will be based on collecting signals near areas of specific river geometries with some consideration to not group any units too closely, but the exact location of nodes will depend on conditions during each seasonal deployment. Prior work suggests that signals for bedload, river discharge, and precipitation can be adequately partitioned in order to use seismic signals to generate rating curves for bedload sediment flux in a high-gradient river. At a minimum we expect to relate the appropriate signal for sediment transport to overall magnitudes of transport, and relate those timings to landform evolution trends in the active floodplain on an inner-annual basis. Currently I am looking for one L-22 and one L-28 for full-time occupation near Longmire, and 10 of the IGU-16HR nodes to be dispersed up and down the river. Prior studies for bedload estimates employed the L-28 but claimed the signal attenuated significantly above 100 Hz, my hope is to rule out the need for the high-frequency data in such projects. The area in question near Longmire is free of Wilderness and Historic District restrictions common to most of the Park, and is a candidate for longer occupations and ground disturbance for instrument burial. I am hoping to get a bit more guidance on what I might need for data loggers and power for these units, understanding that housings and power are still something I need to supply. Most of the remaining study area is designated Wilderness and would carry significant permitting burdens to have buried sensors, making the all-in-one nodes more attractive. This is the same reason the USGS has relied on nodes for similar projects on Mount Rainier and Mount St. Helens. The IGU-16HR units in particular appear to have modular batteries which makes them more suitable for extended occupations beyond the normal one-month deployment, with the goal of capturing as much of the season from May-Oct. as possible. My primary technical collaborator for seismology needs related to this project is Weston Thelen of the USGS Cascades Volcanoes Observatory, and remaining support for field work and geomorphology analysis will be provided by myself and the Mount Rainier National Park Imminent Threats Program.
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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 | 0.000 |
| 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) | 0.001 | 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".