{"id":"W2920772632","doi":"10.1109/tbdata.2019.2903092","title":"Incremental Deep Computation Model for Wireless Big Data Feature Learning","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Big Data","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Big data; Artificial intelligence; Deep learning; Machine learning; Wireless network; Computation; Wireless; Feature (linguistics); Data modeling; Algorithm; Data mining","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":[0.0004686581,0.0001847207,0.0001824392,0.0001475232,0.0003547548,0.0002122799,0.002416507,0.00009921364,0.000005667067],"category_scores_gemma":[0.00001040928,0.0001810414,0.00004191955,0.0002873999,0.00002325141,0.0007969756,0.00006716264,0.0004094662,0.0001006964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003768921,"about_ca_system_score_gemma":0.00008701526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000103443,"about_ca_topic_score_gemma":0.0002137306,"domain_scores_codex":[0.9982457,0.00008682853,0.0001891493,0.0008691907,0.0003199292,0.0002892145],"domain_scores_gemma":[0.9976034,0.0001518613,0.00009525567,0.002014148,0.00004915677,0.00008619335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002909328,0.0001042414,0.00002811466,0.00003302027,0.00003534155,9.606423e-7,0.0002165261,0.2845484,0.000488211,0.00004429457,0.00162112,0.7128506],"study_design_scores_gemma":[0.0007309355,0.00009284888,0.00005853166,0.00003165719,0.00002445018,0.000009163141,0.00003051223,0.9929273,0.0002525043,0.00005400783,0.005571804,0.0002163487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008235817,0.00002535936,0.9882921,0.0008304408,0.001556505,0.0002935491,0.0003931623,0.0002423745,0.0001306771],"genre_scores_gemma":[0.9613696,0.0000220105,0.03617978,0.0002367293,0.0001461208,0.00001149339,0.000976306,0.00002464231,0.001033266],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9531338,"threshold_uncertainty_score":0.7382655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1011934834914356,"score_gpt":0.3053111633478779,"score_spread":0.2041176798564423,"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."}}