Open hardware: Geophysical instrumentation for collaborations in a changing world
Bibliographic record
Abstract
The technologies used in geophysical instruments have allowed the geoscientific community to investigate targets of interest for several decades now. Going forward, geophysics has the potential to play a key role in how we adapt to a warming climate. From assessing geotechnical changes in soils, monitoring contaminants and characterizing aquifers for sustainable use, geophysics can help provide guidance as we adapt to our changing environment. Recognizing that the impacts of climate change can affect small island nations and developing nations disproportionately, our project aims to develop technological solutions that will enable everyone to participate in geosciences by setting up monitoring experiments and participating in the development of global knowledge pool that will enable us to meet the challenges imposed by a changing world. This contribution presents our first geophysical openhardware contribution. We have developed a rugged and versatile 24-bit geophysical logger. The control and synchronization of the acquisition system is engineered around Arduino compatible microcontrollers that are opensource. This provides an intuitive and vibrant development community. A frequency modulated communication protocol allows us to communicate data efficiently and send power over a single conductor wireline. This modular geophysical logger can easily be reconfigured to be used with multiple sensors. The first use case of this logger is a Vertical Seismic Profiling tool chain. The novel design reduces the overall cost and size, which facilitates the deployment of more channels and in configurations that are not feasible with current commercial equipment. The components in this design are easily sourced from electronics distributors and the schematics, printed circuit board layout and custom software library are open source and available at github.com/armercier/Open-seismicelectrical- design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".