Bibliographic record
Abstract
ABSTRACT In the next few years, several new satellite sensors will be launched by various national remote-sensing/earth observation agencies around the globe. It is hoped that these space-borne sensors will provide oil spill response personnel with more than just a synoptic overview of the spill scene. The state-of-the-art capabilities of these new sensors should provide responders with information that can be used in a tactical role as opposed to older-generation sensors that perform a strictly strategic role. Of primary use to spill response coordinators is the Synthetic Aperture Radar (SAR) sensor. The next generation of SAR satellites will have enhanced capabilities when compared to their predecessors. The enhancements include the addition of Polarimetric modes for satellites, including Envisat-1 and RADARSAT-2. RADARSAT-2 will be quad-polarimetric, with resolutions of 8 × 8 m in Polarimetric mode and down to 3 × 3 m in co- or cross-pole modes. The ASAR sensor on Envisat-1 will follow up the successful missions of the European Space Agencies ERS-1, −2 satellites. ASAR will have an alternating polarization mode, and transmit and receive polarization can be selected, thus allowing scenes to be imaged simultaneously in two polarizations. In addition to SAR satellites, several new optical satellites have been or will be launched over the next few years. While optical sensors often are plagued by periods of foul weather that frequently accompany oil spills, some of these sensors will provide valuable information that can be used in conjunction with the radar data in a corroborative fashion. The most useful of the new optical satellites might well be those used to collect data for weather forecasting. This paper will review the operating characteristics and modes of recent and planned satellite sensors, with an eye toward their usefulness for tactical remote sensing of oil spills.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".