{"id":"W2110116143","doi":"10.1109/icc.2007.648","title":"An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Wireless sensor network; Computer science; Association rule learning; Data mining; Process (computing); Data stream mining; Knowledge extraction; Distributed computing; Computation; Computer network; Algorithm","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":[0.005332405,0.001206093,0.002499128,0.004074396,0.001219247,0.001991291,0.003024681,0.001478176,0.001101115],"category_scores_gemma":[0.01761734,0.0008578909,0.001683996,0.00542942,0.0006363838,0.003892943,0.001809163,0.001783612,0.0008844778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004623869,"about_ca_system_score_gemma":0.001770387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001012532,"about_ca_topic_score_gemma":0.001447833,"domain_scores_codex":[0.9957301,0.0007866545,0.0007842684,0.0009314,0.001609742,0.0001578788],"domain_scores_gemma":[0.9901835,0.005113984,0.001149348,0.001801475,0.001602811,0.0001488724],"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":[0.0006587866,0.0005819618,0.005442814,0.0006986766,0.0005827121,0.00106445,0.0003945754,0.08146363,0.02174473,0.01887303,0.007940033,0.8605545],"study_design_scores_gemma":[0.0002515131,0.0004471387,0.002691541,0.0001514171,0.000390213,0.001742328,0.0001941481,0.8985965,0.03234985,0.04427885,0.01878295,0.00012355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006871651,0.000281256,0.9903137,0.0001686012,0.00004378844,0.0001903099,0.0004348455,0.001482922,0.0002129381],"genre_scores_gemma":[0.09346604,0.0004478587,0.9020396,0.0001862118,0.00009101275,0.0007395775,0.002073068,0.00006440354,0.0008922036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005332405,"threshold_uncertainty_score":0.02820081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03380474071331885,"score_gpt":0.3095385861296419,"score_spread":0.275733845416323,"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."}}