{"id":"W4231557727","doi":"10.1109/asonam.2016.7752293","title":"Mining hidden constrained streams in practice: Informed search in dynamic filter spaces","year":2016,"lang":"en","type":"article","venue":"2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Data stream mining; Set (abstract data type); Filter (signal processing); Data stream; Data mining; Dynamic data; Space (punctuation); Tracking (education); Selection (genetic algorithm); Data set; Data science; Artificial intelligence; Database","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":[0.007846035,0.00166198,0.003575549,0.003728784,0.00126112,0.003871301,0.003466997,0.003561109,0.001889794],"category_scores_gemma":[0.03239652,0.00173375,0.001461433,0.004010988,0.002105498,0.007543863,0.003188443,0.002596748,0.0004541086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001753127,"about_ca_system_score_gemma":0.002394013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006474968,"about_ca_topic_score_gemma":0.00686048,"domain_scores_codex":[0.996727,0.001403658,0.0002683459,0.000780071,0.0005519761,0.0002688828],"domain_scores_gemma":[0.9645821,0.03117197,0.001580102,0.001249138,0.0009732833,0.0004433158],"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":[0.0002795534,0.0002348736,0.006089393,0.0002866059,0.0002535647,0.0002736346,0.0004209731,0.8830724,0.0006251516,0.04170511,0.002102692,0.06465605],"study_design_scores_gemma":[0.00001710814,0.00002200671,0.0001269969,0.00001148715,0.000009920811,0.00001613801,0.00003939302,0.976496,0.000127001,0.02285904,0.0002681516,0.000006698815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03140164,0.0007276833,0.9657377,0.0007710895,0.0000305896,0.00009194046,0.0002228177,0.0003301986,0.000686452],"genre_scores_gemma":[0.5573688,0.001094806,0.4365536,0.0005691007,0.0003232569,0.0004257935,0.001550854,0.0001702613,0.001943618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007846035,"threshold_uncertainty_score":0.04149431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02815580947621436,"score_gpt":0.3568444888092165,"score_spread":0.3286886793330021,"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."}}