Concept of a new multiangular satellite mission for improved bi-directional sampling of surface and atmosphere properties
Bibliographic record
Abstract
While existing satellite Earth Observing (EO) systems provide many baseline observations, they are lacking an important combination of capabilities in terms of angular sampling, spectral coverage, and spatial resolution. This has an impact on the accuracy of retrievals and the capability to provide accurate regular operational monitoring of surface and atmospheric properties on a large scale (global or continental). The concept of Advanced Multiangular MEdium Resolution System (AMMERS), that addresses the above issues and provides unique capability presently unavailable from other space observing systems, is proposed here. The mission's unique feature is a combination of medium resolution (400m), multi-spectral observations (13 spectral bands in visible, NIR, SWIR, IR, SW and LW), and multi-angular capabilities (7 angles). AMMERS's multiangular features and swath width allow bi-directional angular sampling close to the solar principal plane and in the perpendicular plane. These capabilities are critically important for accurate estimation of surface albedo, vegetation structure, and forest parameters. AMMERS will also be superior to many other missions in retrieving SST, aerosol, clouds parameters, snow, and wild fire mapping.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".