Integration of the SAR Payload on Board of the Meteor-M No. 2-2 Spacecraft into the COSPAS–SARSAT International Search and Rescue System
Bibliographic record
Abstract
The present article addresses the results of the “Meteor-M” No. 2-2 SAR payload (RK–SM–MKA) commissioning campaign. The international effort was coordinated by Russia with the participation of technical teams from national administrations of the United States, Canada and France. In the course of testing, it was determined that the SAR payload performance was within technical expectations and that the SAR payload could be operationally used in the COSPAS–SARSAT system. Other matters of integrating the “Meteor-M” No. 2-2 (“Cospas-14” in Cospas-Sarsat terminology) SAR payload into the COSPAS–SARSAT system as well as the system’s readiness to accept the new spacecraft are discussed. The article also unveils the objectives addressed during the “Сospas-14” integration period and the benefits gained by the system, which were recently made public by the COSPAS–SARSAT Secretariat. The analysis performed by the Secretariat demonstrated that the addition of the new “Cospas-14” into the LEOSAR system significantly improves LEOSAR satellite latency due to the spatial diversity of the current SARSAT and “Cospas-14” orbits.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".