{"id":"W1771727655","doi":"10.1002/navi.8","title":"A Composite Model for Indoor GNSS Signals: Characterization, Experimental Validation and Simulation","year":2012,"lang":"en","type":"article","venue":"NAVIGATION Journal of the Institute of Navigation","topic":"Satellite Communication Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Ministry of Advanced Education, Government of Alberta","keywords":"GNSS applications; Fading; Computer science; Amplitude; Satellite system; SIGNAL (programming language); Filter (signal processing); Remote sensing; Electronic engineering; Real-time computing; Global Positioning System; Algorithm; Telecommunications; Engineering; Physics; Geography; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0007256672,0.0005871217,0.0005301946,0.0006004294,0.000241582,0.000615877,0.0007047834,0.0008298125,0.001094816],"category_scores_gemma":[0.00123512,0.0001778569,0.000473072,0.0006015472,0.0005010539,0.0005713814,0.0004570566,0.0006028478,0.0002806216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005448223,"about_ca_system_score_gemma":0.0004258907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00360329,"about_ca_topic_score_gemma":0.002831069,"domain_scores_codex":[0.9996227,0.00009045559,0.000016613,0.00005866533,0.0001685727,0.00004293889],"domain_scores_gemma":[0.999413,0.0002471486,0.00005893798,0.000104733,0.0001489001,0.00002727054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001358903,0.00008423318,0.001852168,0.00006104523,0.00001558166,0.00008642476,0.00006197569,0.9735671,0.01160109,0.002111576,0.0002473825,0.01017551],"study_design_scores_gemma":[0.000006278882,0.00007581732,0.000455622,0.000002504892,0.00000563179,0.00002252604,0.00001216127,0.9957241,0.003136228,0.0003018511,0.0002509385,0.000006354035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3502941,0.0001640654,0.6415907,0.0001451582,0.00007178816,0.0001992692,0.0004244622,0.001037024,0.006073534],"genre_scores_gemma":[0.9536811,0.0001279996,0.04370145,0.00002246588,0.000009484577,0.0001455641,0.0002085857,0.00003867481,0.002064838],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00360329,"threshold_uncertainty_score":0.007164598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04564369369759792,"score_gpt":0.3039944458628911,"score_spread":0.2583507521652932,"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."}}