{"id":"W1982974129","doi":"10.1007/s11036-009-0190-5","title":"On Prolonging the Lifetime for Wireless Video Sensor Networks","year":2009,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Wireless sensor network; Real-time computing; Video tracking; Wireless; Sink (geography); Cluster analysis; Wireless network; Key distribution in wireless sensor networks; Computer network; Video processing; Artificial intelligence; Telecommunications","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":[0.001793007,0.001228645,0.001115915,0.0009752756,0.000869466,0.001112769,0.001474537,0.00109327,0.002947599],"category_scores_gemma":[0.008276316,0.0003270242,0.0004396656,0.001441143,0.001341009,0.005283328,0.001334112,0.001451944,0.0003826308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00130402,"about_ca_system_score_gemma":0.0005622395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002465884,"about_ca_topic_score_gemma":0.002745432,"domain_scores_codex":[0.9992685,0.0002137079,0.00004741721,0.000108278,0.000252575,0.0001096321],"domain_scores_gemma":[0.9968373,0.002266583,0.000143478,0.0001936415,0.0004699961,0.00008907206],"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":[0.0006640673,0.00009080017,0.0006507158,0.001107821,0.00009380791,0.0003256667,0.0005231397,0.2292919,0.01534239,0.263822,0.02625785,0.4618298],"study_design_scores_gemma":[0.00004629082,0.0002961186,0.0004535728,0.0001907459,0.00009615346,0.0003034097,0.0001973989,0.7486991,0.007276802,0.2053461,0.03703948,0.00005482485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03208382,0.07057407,0.8689334,0.00708455,0.002898452,0.0001291875,0.0001683358,0.0003365756,0.01779162],"genre_scores_gemma":[0.7302119,0.08228915,0.1540109,0.001733772,0.004149182,0.0003012887,0.0003328765,0.0004928861,0.02647807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002947599,"threshold_uncertainty_score":0.009860694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005308333781799178,"score_gpt":0.2242061588250158,"score_spread":0.2188978250432167,"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."}}