{"id":"W2352946880","doi":"10.1007/s12652-016-0380-5","title":"Continuous objects detection and tracking in wireless sensor networks","year":2016,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Wireless sensor network; Tracking (education); Continuous monitoring; Video tracking; Boundary (topology); Base station; Object detection; Real-time computing; Object (grammar); Visual sensor network; Artificial intelligence; Computer vision; Wireless; Data mining; Key distribution in wireless sensor networks; Wireless network; Pattern recognition (psychology); Computer network; Telecommunications; Mathematics","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":[0.001302608,0.0005232223,0.0009078316,0.000977328,0.0005695981,0.001272061,0.001649694,0.00139638,0.0008152401],"category_scores_gemma":[0.00643748,0.0005951197,0.0003481763,0.001488085,0.001047765,0.002705826,0.001286581,0.001016111,0.0002906529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005971208,"about_ca_system_score_gemma":0.0005156954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002873076,"about_ca_topic_score_gemma":0.002408181,"domain_scores_codex":[0.9986982,0.0002525228,0.00007864472,0.0003779038,0.0004985415,0.00009405155],"domain_scores_gemma":[0.9969715,0.001958186,0.000333152,0.0002625843,0.0003637443,0.0001108134],"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":[0.0006197761,0.0002862305,0.00654272,0.0002965461,0.0001500274,0.0003442788,0.0003475281,0.4919791,0.03560079,0.018199,0.002338373,0.4432957],"study_design_scores_gemma":[0.000007645192,0.00004579598,0.001347075,0.000007788772,0.00001334051,0.00007892794,0.0000324765,0.9896358,0.003009951,0.005185223,0.0006258601,0.00001016983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06443521,0.001754896,0.9318243,0.0002673636,0.0001652926,0.00003558109,0.00006140144,0.0002612678,0.001194689],"genre_scores_gemma":[0.9020531,0.001320197,0.09138796,0.00009564922,0.0002145063,0.0000952725,0.0001511133,0.00004643724,0.00463582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002873076,"threshold_uncertainty_score":0.006888986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01745546715443424,"score_gpt":0.2454518216673623,"score_spread":0.2279963545129281,"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."}}