REMOTE: Geodetic metrology for future accelerators - Geodetic techniques for determining position and orientation with high accuracy
Bibliographic record
Abstract
<!--HTML--> Abstract A core service of applied geodesy are the determination of coordinates and orientation with respect to chosen reference frames and the derivation of information including quality indicators therefrom. This is required for digitizing the 3d world, for transferring plans and models from the virtual space into the real one, and for reliably quantifying deformations and rigid body motion over time. The lecture will give a brief overview about techniques and solutions. Emphasis will be put on instrumental, environmental and practical accuracy limitations for coordinate measurements using GNSS, total stations and other standard geodetic instruments, as well as on limitations for azimuth and coordinate transfer from above ground to underground infrastructure. Short Bio Andreas Wieser Andreas Wieser is a professor of Geosensors and Engineering Geodesy at ETH Zurich since 2012. He has over 20 years of experience in research and teaching from Universities in Austria, Canada and Switzerland. He has been a product manager for GPS-based tolling. His research covers high-precision GNSS, parameter estimation, quality control and calibration, digitization of reality, geodetic monitoring, and the development of novel measurement systems. Since about ten years he focusses on the development of innovative laser-based sensor technology, and point-cloud processing. He is the Chair of the Society for the Calibration of Geodetic Devices, a member of the Swiss, Austrian and German Geodetic Commission, and of the Swiss Federal Surveyor’s Commission.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.092 | 0.077 |
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".