{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005465632,0.0006144201,0.0005689234,0.000426833,0.0003236006,0.0006704965,0.001727516,0.0005683416,0.001519229],"category_scores_gemma":[0.001875266,0.0003069227,0.0006251965,0.0006036498,0.0005642634,0.00175902,0.0008474345,0.001555053,0.0003227625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008694557,"about_ca_system_score_gemma":0.001272241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007901137,"about_ca_topic_score_gemma":0.009478655,"domain_scores_codex":[0.9996936,0.00004226022,0.00001900796,0.00008720154,0.0001017826,0.00005626328],"domain_scores_gemma":[0.999544,0.0001496208,0.00004329224,0.00007085258,0.0001622381,0.00002993634],"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":[0.0001211191,0.0001032485,0.002491185,0.00009489278,0.00006268908,0.0001447199,0.0001010166,0.7866994,0.004461512,0.03541032,0.004079319,0.1662306],"study_design_scores_gemma":[0.000001891886,0.00001015859,0.00007688801,0.000001867177,0.00000507808,0.00001202,0.000002901646,0.9953524,0.0004388779,0.003723276,0.0003719677,0.000002685601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01824291,0.0003054935,0.9788831,0.0002847743,0.0000690097,0.0000397119,0.0001040147,0.0005109787,0.001560075],"genre_scores_gemma":[0.809831,0.0006546335,0.1830256,0.0002775199,0.00008569726,0.000234243,0.000527632,0.00009698648,0.005266538],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007901137,"threshold_uncertainty_score":0.01571029,"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."}}