Bibliographic record
Abstract
Imaging radar is a unique remote sensing system in that it uses its own source of target illumination, therefore providing imagery independent of solar illumination, and operates at a wavelength long enough to be able to penetrate clouds, making it insensitive to weather. The raw ground resolution of an imaging radar is far too coarse to be useful in identification of terrestrial targets, but mathematical recombination of all radar returns from a target while it is in the field of view of the sensor allows the computation of a synthetic aperture many kilometers long, and hence improves the resolution of the sensor to a few meters. Multichannel synthetic-aperture radar (SAR) is achieved through the sending and receiving of different polarizations of radar signal. After suitable noise filtering, polarimetric SAR responses can be decomposed to infer scattering types: surface, dihedral, and volume scatterers. From these decompositions, traditional classification techniques may be used to identify features on the ground, both discrete scatterers—strongly reflecting point objects like towers, poles, or other man-made structures—and distributed scatterers—fields, forests, and other natural environments. Examples are given, including identification of distributed scatterers in a region of Chinese Inner Mongolia, invasive weed growth in a prairie region in southern Alberta, Canada, and oil and gas infrastructure in central Alberta, Canada.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.038 |
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".