The Warr Machine: System Design, Implementation and Data
Bibliographic record
Abstract
ABSTRACT In this paper, we describe a ground penetrating radar (GPR) system called the wide angle reflection and refraction (WARR) machine, outline the design and discuss the implementation challenges. WARR and the closely related common-mid-point (CMP) GPR soundings have been standard survey methods to measure velocity since GPR first existed. Earliest efforts demonstrated the variation in ice sheet velocity versus depth. Although GPR multi-offset soundings are valuable survey methods, they have seen little adoption since many systems are not bistatic. In addition, surveys most often use a single transmitter with a single receiver deployed sequentially at varying antenna separations, making data acquisition slow. Modern instrumentation with recent advances in GPR timing and control technology has enabled deployment of systems with multiple concurrent sampling receivers. This development has resulted in the ability to continuously acquire multi-offset WARR data at the same rate as two dimensional (2D) common offset reflection surveys in the past. The concomitant issues of survey design plus organizing the WARR data storage, documentation and analysis present numerous challenges. The extraction of velocity information from the large volumes of GPR WARR/CMP data demands automated analysis techniques. We have explored the use of normal move out (NMO) stacking at creating enhanced zero offset section from multi-offset data. Furthermore, we investigated the use of semblance analysis at estimating move-out velocities in order to apply in the NMO stack. These traditional seismic processing steps have proven to be less effective with GPR. These conclusions point to the differences in data character between seismic and GPR. Results of in-field deployment are used to illustrate advances to date and point the way to further advancements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".