{"id":"W2605042189","doi":"10.22323/1.236.0638","title":"EUSO-Balloon: Observation and Measurement of Tracks from a Laser in a Helicopter","year":2016,"lang":"en","type":"preprint","venue":"Proceedings of The 34th International Cosmic Ray Conference — PoS(ICRC2015)","topic":"Astrophysics and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Helmholtz Alliance for Astroparticle Physics; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Ministerio de Ciencia e Innovación; Centre National d’Etudes Spatiales; Russian Foundation for Basic Research; Japan Society for the Promotion of Science; RIKEN; National Aeronautics and Space Administration; Universidad Nacional Autonoma de Honduras; Slovenská Akadémia Vied; Strong; Comunidad de Madrid","keywords":"Laser; Altitude (triangle); Physics; Remote sensing; Payload (computing); Cosmic ray; Balloon; Detector; Optics; Atmosphere (unit); Observatory; Near space; Field of view; Photon; Astronomy; Meteorology; Geology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001998154,0.000250584,0.0001879002,0.0009952765,0.0004920725,0.0003289014,0.0003474584,0.0004621628,0.0008439585],"category_scores_gemma":[0.0002125711,0.0001505502,0.0001559117,0.0007195341,0.0002163666,0.0002189221,0.0002747427,0.0002701083,0.000240375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005046148,"about_ca_system_score_gemma":0.0003766641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009795774,"about_ca_topic_score_gemma":0.02052594,"domain_scores_codex":[0.9998529,0.00001475993,0.000003748523,0.00004528826,0.00005174425,0.0000315734],"domain_scores_gemma":[0.999826,0.00001630848,0.00003373323,0.00002139637,0.00005906454,0.00004345902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001398761,0.0003373441,0.2517878,0.0001279441,0.000160212,0.001164038,0.001496373,0.006779361,0.6671177,0.0007532996,0.003251653,0.06562545],"study_design_scores_gemma":[0.0001343244,0.001176969,0.8280804,0.00004784304,0.0001215073,0.001217507,0.0006822563,0.04830391,0.1114161,0.0001886724,0.008555425,0.00007519692],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934713,0.0001064067,0.003313717,0.00003182885,0.00001221902,0.00003873149,0.001072775,0.0002362302,0.001716884],"genre_scores_gemma":[0.9880422,0.00007452679,0.009981436,0.0000250769,0.000008923718,0.00002140315,0.0009810334,0.0000279047,0.000837408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009795774,"threshold_uncertainty_score":0.01947755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03120899679704275,"score_gpt":0.2373860189055982,"score_spread":0.2061770221085554,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}