Next-Generation Quantum Gravity Sensors for Geophysical Applications: New Insights from the FIQUgS Project
Bibliographic record
Abstract
Summary The FIQUgS project, funded by the EU’s Horizon Europe initiative, aims to revolutionize geophysical applications through next-generation quantum gravity sensors. These advanced tools, including the Absolute Quantum Gravimeter (AQG) and Differential Quantum Gravimeter (DQG), address limitations of earlier sensors by improving portability, robustness, and precision. They enable high-accuracy measurements of gravity and gravity gradients for diverse applications, such as monitoring groundwater, detecting subsurface voids, and studying volcanic activity. Significant milestones include hardware advancements and a sophisticated software suite. This suite features tools for survey planning, real-time and post-survey data processing, and 3D mass density modeling. Notably, the DQG sensor demonstrated its potential during an archaeological study in Lisbon, Portugal, where it identified a Roman-era tunnel with high accuracy. By integrating gravity and gravity gradient data, the FIQUgS tools minimized environmental noise and enhanced subsurface feature characterization. This success underscores their transformative impact on geophysical research and industrial applications, paving the way for broader adoption of quantum gravity technology in multidisciplinary fields.
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 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".