{"id":"W2920267096","doi":"","title":"A context-aware machine learning-based approach","year":2018,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Context (archaeology); Artificial neural network; Set (abstract data type); Context model; Control (management)","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":[0.0008630335,0.0001988399,0.0001787869,0.0003235704,0.0003361598,0.0005088143,0.001565734,0.00005104811,0.000002083506],"category_scores_gemma":[0.0002733296,0.0001858512,0.00002944812,0.0009659621,0.0003361128,0.0008781533,0.0008439545,0.0002019635,0.00001043653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005528193,"about_ca_system_score_gemma":0.0001375485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001985995,"about_ca_topic_score_gemma":0.000001242609,"domain_scores_codex":[0.9982619,0.00002211257,0.0001680524,0.0006765058,0.0004332568,0.0004381572],"domain_scores_gemma":[0.9987812,0.0001234821,0.00005073813,0.0006143551,0.0002426258,0.0001876078],"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":[0.000007894541,0.0001479015,0.01109621,0.0001387154,0.00002679743,0.0000523452,0.002448644,0.008538254,0.0005378277,0.01207585,0.002298852,0.9626307],"study_design_scores_gemma":[0.0001378123,0.0001869799,0.001160086,0.00004277944,0.000001999075,0.00003387258,0.000002537468,0.9910986,0.001812426,0.00003823743,0.005243346,0.0002413046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00445451,0.00008164454,0.9933879,0.00007098457,0.0002921363,0.0001058391,0.000002597613,0.001577663,0.00002666522],"genre_scores_gemma":[0.5003529,0.000001711989,0.4993271,0.0002111979,0.00008039361,0.000008730105,0.000002639812,0.000008449204,0.000006878937],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9825604,"threshold_uncertainty_score":0.757879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01083987578725308,"score_gpt":0.211665959954587,"score_spread":0.200826084167334,"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."}}