{"id":"W4206662593","doi":"10.1049/cmu2.12322","title":"Coordinated 3D spectrum utilization for B5G indoor HetNets: A collaborated crowdsensing approach","year":2021,"lang":"en","type":"article","venue":"IET Communications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Crowdsensing; Computer science; Computer network; Heterogeneous network; Wireless; Telecommunications; Wireless network; Data science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006876612,0.0001439346,0.0002284841,0.0001017508,0.0005701434,0.0003354329,0.001187223,0.00009811443,0.000005123531],"category_scores_gemma":[0.0005231892,0.0001582882,0.00006083766,0.002546387,0.00008882879,0.0003457543,0.0004398384,0.0001890445,0.00001023103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006527708,"about_ca_system_score_gemma":0.0003639301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003181675,"about_ca_topic_score_gemma":0.0001481142,"domain_scores_codex":[0.9983695,0.0005237887,0.000328941,0.0003576195,0.0001407616,0.0002794278],"domain_scores_gemma":[0.995927,0.0004892622,0.0001505548,0.002518702,0.0008441193,0.0000703543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000897744,0.00331972,0.01002784,0.0002899035,0.0007086348,0.00003326674,0.008872873,0.004920272,0.02607044,0.6503633,0.01923382,0.2760702],"study_design_scores_gemma":[0.001879813,0.00009536007,0.01021176,0.00009329552,0.00004963549,0.00009619446,0.0003695873,0.825228,0.01737761,0.01269824,0.1311928,0.0007076428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00198897,0.001132829,0.9844427,0.00607504,0.0002352305,0.0003511036,0.00002874244,0.0003529809,0.005392478],"genre_scores_gemma":[0.5190451,0.0001398626,0.4800558,0.000317747,0.00002335004,0.00004839402,0.0002067911,0.00001488269,0.0001480756],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8203077,"threshold_uncertainty_score":0.6454806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1262847010364642,"score_gpt":0.3619478327010616,"score_spread":0.2356631316645974,"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."}}