{"id":"W2133544364","doi":"10.1145/1266894.1266907","title":"A system for semantic data fusion in sensor networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Computer science; Wireless sensor network; Sensor fusion; Semantic technology; Context (archaeology); Semantic computing; Sensor web; Process (computing); Semantic data model; Data mining; Distributed computing; Key distribution in wireless sensor networks; Real-time computing; Semantic Web; Artificial intelligence; Computer network; Telecommunications","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":[0.001471976,0.0001537948,0.0002089116,0.0001605939,0.0000921085,0.0001007862,0.001592668,0.0001246648,0.000002131749],"category_scores_gemma":[0.00003260743,0.0001347819,0.00003936813,0.0006523139,0.0000219441,0.0002682112,0.000689349,0.0001323786,0.00001211966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007523775,"about_ca_system_score_gemma":0.00002095831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007839894,"about_ca_topic_score_gemma":0.0004809845,"domain_scores_codex":[0.9980649,0.00004721103,0.000393477,0.000658671,0.0002319591,0.0006037563],"domain_scores_gemma":[0.997744,0.0004622137,0.00008916893,0.001543711,0.00005957552,0.0001012892],"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":[0.00004962016,0.0001559766,0.001585613,0.00009343332,0.00001730668,0.0001714438,0.000161609,0.8736202,0.0004484478,0.07855961,0.003482593,0.04165422],"study_design_scores_gemma":[0.0004214179,0.00002789474,0.0007111022,0.00008622582,0.00000343486,0.00002772961,0.00007203624,0.9967211,0.0002868416,0.000009111476,0.001456392,0.0001766996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01686012,0.0001194981,0.9794854,0.0001690336,0.0009090545,0.0003102427,0.000001654454,0.0003441139,0.001800863],"genre_scores_gemma":[0.8552665,0.000007623039,0.1440108,0.0001540058,0.0002087171,0.000005246553,0.00002544849,0.00001542456,0.0003062425],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8384064,"threshold_uncertainty_score":0.5496247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02623345656924418,"score_gpt":0.2610517357776937,"score_spread":0.2348182792084496,"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."}}