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Cospas-Sarsat Satellite System for Search and Rescue

2008· book-chapter· en· W2476075611 on OpenAlexaff
James V. King

Bibliographic record

VenueAdvances in electronic commerce (AEC) book series/Advances in electronic commerce series · 2008
Typebook-chapter
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsSearch and rescueBeaconSatelliteSatellite systemSatellite constellationGlobeDistressComputer scienceGeographyTelecommunicationsComputer securityGlobal Positioning SystemEngineeringMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter outlines the development and evolution of the Cospas-Sarsat system, describes the principle of operation, presents the current status and looks at the future of the system. Cospas-Sarsat, an international satellite system for search and rescue, started operating in 1982 and has been credited with saving many thousands of lives since then. More than a million aviators, mariners and land users worldwide are equipped with Cospas-Sarsat distress beacons that could help save their lives in emergency situations anywhere in the world. A constellation of satellites is circling the globe monitoring for distress signals, while tracking stations on six continents receive the satellite signals, compute the location of the emergency and quickly forward the distress alert information to the appropriate rescue authorities. This is a big improvement over the pre-satellite era, when distress signals from remote regions or far out at sea might not have been heard for many days or even weeks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.099
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0990.094

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.

Opus teacher head0.007
GPT teacher head0.232
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2008
Admission routes1
Has abstractyes

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