{"id":"W1927015685","doi":"10.1109/mcom.2015.7105664","title":"Obtaining infrared spectral imagery of the upper atmosphere using a cubesat","year":2015,"lang":"en","type":"article","venue":"IEEE Communications Magazine","topic":"Spacecraft Design and Technology","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"CubeSat; Atmosphere (unit); Payload (computing); Remote sensing; Computer science; Satellite; Environmental science; Infrared; Meteorology; Aerospace engineering; Geology; Computer security; Optics; Physics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004553355,0.0005273739,0.0002615322,0.001268422,0.0008382617,0.001011147,0.0004087562,0.0003262926,0.006591323],"category_scores_gemma":[0.0004353223,0.0003022602,0.0004331609,0.001379617,0.0002959385,0.0008707635,0.0009262612,0.000862764,0.00259428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005133036,"about_ca_system_score_gemma":0.0009809162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01200974,"about_ca_topic_score_gemma":0.0445869,"domain_scores_codex":[0.9995453,0.00002371566,0.00001286339,0.00006154238,0.0002996421,0.00005690306],"domain_scores_gemma":[0.9996897,0.00002420019,0.00001963147,0.00006762458,0.0001603466,0.00003847132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002224673,0.0001731021,0.01619406,0.0001768281,0.0001302884,0.0003936306,0.0006539276,0.006899778,0.6940372,0.002466259,0.03954361,0.2391089],"study_design_scores_gemma":[0.0001179441,0.0003731984,0.1289916,0.000130812,0.0002016815,0.001486782,0.001532095,0.06585957,0.5494508,0.004795753,0.2467928,0.0002668589],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4465484,0.0009068442,0.3346777,0.002467947,0.0004499661,0.0006642966,0.02109994,0.01826252,0.1749224],"genre_scores_gemma":[0.3381482,0.0007217621,0.6307524,0.0009527438,0.0001099975,0.0001780077,0.01538305,0.001446339,0.01230738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01200974,"threshold_uncertainty_score":0.02387965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04403539629623125,"score_gpt":0.2657975014546761,"score_spread":0.2217621051584449,"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."}}