{"id":"W2917651750","doi":"10.3390/s19040937","title":"Big Data-Driven Cellular Information Detection and Coverage Identification","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Beijing Union University; Beijing University of Posts and Telecommunications; Key Laboratory of Universal Wireless Communications of Ministry of Education; Ministry of Science and Technology","keywords":"Computer science; Identification (biology); Base station; Cellular network; Big data; Granularity; Data mining; Service provider; Service (business); Core network; Computer network; Real-time computing","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00031985,0.0006564329,0.0005920025,0.00106282,0.0003930611,0.0008420082,0.001103251,0.0005876463,0.0005235114],"category_scores_gemma":[0.001686314,0.0002581954,0.000422279,0.0009665678,0.0003208139,0.0008872189,0.0009251376,0.0005544673,0.0002404502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005695704,"about_ca_system_score_gemma":0.0005637925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0034772,"about_ca_topic_score_gemma":0.003986672,"domain_scores_codex":[0.9996496,0.0000543415,0.00001823865,0.0001074206,0.0001239215,0.00004639638],"domain_scores_gemma":[0.9990815,0.0003354803,0.0001279435,0.0001331852,0.0002576607,0.00006436395],"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":[0.0003204872,0.0002538016,0.02326659,0.000275203,0.0001497481,0.0005424383,0.0003164363,0.5171718,0.02689546,0.01226883,0.006119285,0.41242],"study_design_scores_gemma":[0.000003720067,0.00001885509,0.001322531,0.000003795479,0.000006414668,0.00006560374,0.00003352485,0.9930521,0.003373592,0.001491246,0.0006218863,0.00000679666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06513467,0.0002957761,0.9311494,0.0002568897,0.0001117076,0.00009284827,0.0002998766,0.0008529826,0.001805781],"genre_scores_gemma":[0.8504502,0.0002004543,0.1464755,0.0001255221,0.0001078421,0.0001634295,0.0006658325,0.00003800337,0.001773167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0034772,"threshold_uncertainty_score":0.0069139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02313299144115182,"score_gpt":0.2596501107689988,"score_spread":0.236517119327847,"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."}}