A Cost-Efficient, Highly Modifiable Borehole Tilt Sensor for Borehole Geophysical Studies
Bibliographic record
Abstract
We present a cost-efficient tilt sensor that was originally developed by our team at Dartmouth College to study ice deformation as part of the Jarvis Glacier Project, and we showcase our successful initial run that includes the development, deployment, and data collection processes. In this case study, we installed our tilt sensor system in two boreholes drilled close to the lateral shear margin of Jarvis Glacier in Alaska and successfully collected over 16 months of uninterrupted borehole deformation data in a harsh polythermal glacial environment. The data included gravity and magnetic data that we used to track the orientation of our sensors in the boreholes over time, and the resultant kinematic measurements enabled us to compute borehole deformation. While our sensors were applied under polythermal thermal regime conditions, we present use cases for our sensors in a variety of glacier thermal regimes including Athabasca glacier, a temperate glacier in Canada, and in Antarctic regions with similar polythermal regimes such as ice streams and outlet glaciers. Sensors embedded in our tilt sensors can be modified to suit different needs, and the tilt sensor can also be modified for different boreholes and glacier conditions. Our goal is to improve the accessibility of borehole geophysics research mainly through supporting production efforts of our sensor for various research needs. With an established sensor development plan, successful applications in the field, and years of experience, our team is open to potential research collaborations with researchers who are interested in using our tilt sensors. Our team is working with Polar Research Equipment, a Dartmouth alumni founded company that specializes in the development of polar research tools, that will serve as a commercial resource for researchers who may require support during the development process or mass-production of our cost-efficient (~20% the price of other commercial versions) yet effective tilt sensors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".