Improving the Accessibility of Borehole Geophysics: A Cost-Efficient, Highly Modifiable Borehole Tilt Sensor
Bibliographic record
Abstract
We present a cost-efficient borehole tilt sensor that was developed by our group at Dartmouth College to study ice deformation on Jarvis Glacier in Alaska. We first detail the entire sensor development, deployment, and data collection process, along with showcasing successful use cases of our sensors on Jarvis and other glaciers both by our and other geophysical research groups. For our Jarvis work, we installed our tilt sensor system in two boreholes drilled close to the lateral shear margin of Jarvis Glacier and successfully collected over 16 months of uninterrupted borehole deformation data in a harsh polythermal glacial environment. The data included gravity and magnetic measurements that tracked the orientation of the sensors in the borehole as ice flows, and we used the resultant kinematic measurements to compute borehole deformation that provided insights into the ice flow dynamics on polythermal glaciers. Our tilt sensors can house many types of sensors to accommodate different scientific needs (e.g., temperature, pressure, electrical conductivity), and can be adapted for the different glacial thermal regimes and conditions like Athabasca Glacier in Canada, which is a temperate glacier in contrast to Jarvis’ polythermal regime. There remains a high knowledge and financial barrier to entry for borehole geophysics research for both development and procurement of a tilt sensor system, and our goal is to lower this barrier by supporting production efforts of our tilt sensor system for both research and educational needs. With our established sensor development plan and demonstrated success in the field, our group has collaborated with Polar Research Equipment (PRE), a Dartmouth alumni-founded company specializing in the development of polar research tools, to serve as a commercial resource to help support polar researchers during the development and/or production of an effective and cost-efficient (~80% cheaper than commercial versions) tilt sensor and its associated systems.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".