{"id":"W1991966706","doi":"10.1109/infcomw.2014.6849242","title":"Content Relevance Opportunistic Routing for Wireless Multimedia Sensor Networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council; British Columbia Institute of Technology","keywords":"Computer science; Routing protocol; Computer network; Network packet; Relevance (law); Wireless sensor network; Routing (electronic design automation); Bandwidth (computing); Dynamic Source Routing; Multimedia; Efficient energy use; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007920525,0.000268063,0.0003928884,0.00004972302,0.0002608742,0.0001894362,0.0007265674,0.0001507117,0.00002051847],"category_scores_gemma":[0.00007961001,0.0002311687,0.0001375595,0.0001697694,0.00008717523,0.0002373169,0.0001907402,0.0002009061,0.00003114126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003570538,"about_ca_system_score_gemma":0.00007047159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002373472,"about_ca_topic_score_gemma":0.000008895862,"domain_scores_codex":[0.9977772,0.00008053696,0.000527391,0.0006380269,0.0002695794,0.0007073008],"domain_scores_gemma":[0.9970696,0.001391907,0.0002224837,0.0007329258,0.000225191,0.0003579094],"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":[0.00003118894,0.00007133904,0.0003726438,0.00002266092,0.00003178491,0.000024228,0.0001143422,0.0006167394,0.0001091941,0.1983433,0.006138565,0.7941241],"study_design_scores_gemma":[0.0007263364,0.0001120221,0.00008581698,0.00004644433,0.00001612851,0.00002292038,0.00003620852,0.9911913,0.00002756497,0.0007406606,0.006657183,0.0003374203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006972957,0.00003139473,0.9904642,0.0006949016,0.001360962,0.0004149539,0.000004805663,0.0003918611,0.005939655],"genre_scores_gemma":[0.8500388,0.00003135251,0.1438511,0.001817943,0.0005413759,0.00004613659,0.00002393364,0.00002589851,0.003623541],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9905745,"threshold_uncertainty_score":0.9426787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05131735046577476,"score_gpt":0.2489430365352619,"score_spread":0.1976256860694871,"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."}}