Bibliographic record
Abstract
On the morning of June 26, 1978 a satellite was launched into earth orbit from Vandenburg Air force Base near Lompoc, California. The Satellite, opened a new age of space remote sensing using active radar to image and probe planetary processes. began as a rough theme to use an array of active and passive microwave technologies, largely untried in space, to remotely sense synoptic ocean properties. This challenging prospect was the dream of scientists, aligned as the NASA sanctioned SEASAT Users Working Group, that had worked for more than five years to develop the appropriate sensor technologies and experiments for SEASAT. This group had staunchly championed the program throughout the approval and development process. This paper describes the epic mission from its origin as an idea in 1972 until it became a reality collecting global ocean data in 1978. The path of the program is traced step-by-step through: definition studies performed by NASA Centers and the Navy Applied Physics Laboratory; establishment of the NASA Program Office in 1974 to develop the plan to secure mission level status and funding; identity as an approved new program in 1975; the selection of an industry team to implement under the guidance of the Jet Propulsion Laboratory; International expansion of the program with scientific participation and mission contributions (data collection stations, tracking sites, experiments) from Canada, Europe and Australia; the successful launch of in June, 1978 initiating a host of scientific demonstration and validation experiments; and the unexpected demise of after 110 days in space. The paper concludes with a view of SEASAT's heritage expressed in terms of the derivative missions that have followed. I. ORIGINS Remote sensing of earth surface phenomena was first accomplished from space in the 1960's on military and NASA satellites using mostly cameras and camera-like imaging devices (passive radiometers). These early remote sensors in space were used to collect and discriminate radiated and reflected electromagnetic energy largely in the visible or infrared spectra (roughly 0.4-micron to 20-micron wavelengths). In 1973, the NASA Earth Resources Technology Satellite (ERTS-1) later renamed the LandSat satellite, initiated a series of missions featuring fine resolution (10's of meters) optical imagers with many visible and infrared channels that were thematically associated with specific land applications. Although these radiometers are capable of providing fine surface spatial resolution and excellent multi-spectral details, they were inhibited by clouds and depend on sun illumination, and are thus limited to daylight observation. The limitations of clouds and darkness can be overcome by moving to the microwave portion of the spectrum. Microwaves are capable of passing through the clouds and permitting unobstructed observations of the earth's surface, including day or night detection. was based on the premise that microwave sensors, active and passive, were more attune to the needs of the ocean and arctic scientific community. Oceanic and ice observations are ideally suited to space-based radar due to: the vast geographic scale and ever changing nature of the subjects. The ability of radar to penetrate persistent cloud cover over regions important to transport and other commerce have produced improvements in nowcasts, forecasts and climatological analyses because diurnal and seasonal variances can be monitored. Since SEASAT, microwave application candidates extend well beyond oceanic and ice observation to include such things as crop and forest monitoring, land management and development, hydrology, disaster management, and an ever growing list of applications important to science and commerce.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".