Bibliographic record
Abstract
The advantages of the synthetic aperture radar (SAR) have been well known among remote sensing data users since the launching of SEASAT in 1978. However, one of the major limiting factors has been the limited spectral resolution with only a single frequency and single polarization data, even from the most sophisticated space-borne SAR systems today. The AIRSAR system, developed by NASA (JPL) in the late 1980s and became fully operation in 1990, has been a unique and valuable tool for the investigators interested in multiple frequency fully polarimetric SAR experiments. The PACRIM-I experiment in selected Pacific Rim countries in 1996 was a great success and the current PACRIM-II is the successor of PACRIM-I. In view of the new space-borne SAR systems: ENVISAT (ESA) ALOS (Japan) and RADARSAT-II (Canadian Space Agency) planned for launching in the 2002 and 2003 time frames, are all polarimetric SAR systems, and it is not only timely but also essential for any remote sensing community to develop polarimetric SAR application capabilities. The Korean participants in PACRIM-II include both the AIRSAR research teams and MASTER hyperspectral research team. The main objectives of the Korean participation in PACRIM-II include establishment of scientific and engineering knowledge base for the polarimetric SAR technology, training of qualified graduate students, and developments of new polarimetric SAR applications. Science and engineering disciplines participating in the Korean PACRIM-II experiment included agriculture, archeology, land use, forestry, geography, geology, geohydrology, coastal science, oceanography, environmental applications, natural disaster monitoring and disaster management.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".