Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ground‐penetrating radar (GPR) is a remote sensing technique that enables field observation and investigation of embedded near‐surface objects, structural discontinuities, and other material heterogeneities. GPR is commonly used to detect embedded targets of interest (objects and structures) with varying material properties, geometries, and depths. Various scientific and commercial applications of GPR exist to identify soil and geologic characteristics, metallic objects, buried artifacts, and even tree roots. GPR antennae send electromagnetic energy in waves into various media, such as soil, rock, concrete, asphalt, and ice, and receive energy reflected from embedded targets or materials that absorb and redirect electromagnetic energy. The data obtained are rendered in a two‐dimensional radargram image, where the horizontal and vertical location of reflector features is represented in an upright‐oriented profile. GPR is most likely to identify targets accurately when the scanning medium is relatively homogeneous; complex subsurface media can challenge successful detection. Evolving data processing methods, informed by simulations and even artificial intelligence, may improve interpretation accuracy in challenging scanning contexts.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it