{"id":"W2121419603","doi":"10.1109/glocom.2009.5425419","title":"Spatial Inference Using Networks of RFID Receiver: A Bayesian Approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Inference; Statistical inference; Bayesian probability; Computation; Bayesian inference; Statistical model; Position (finance); Data mining; Approximate Bayesian computation; Bayesian network; Artificial intelligence; Algorithm; Mathematics; Statistics","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":[0.004777384,0.001048784,0.001701044,0.002613817,0.0009535085,0.002197569,0.004162475,0.001764978,0.002519656],"category_scores_gemma":[0.02110608,0.001366688,0.001478498,0.002493636,0.001892361,0.005453915,0.00270992,0.002505624,0.001042236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001484189,"about_ca_system_score_gemma":0.001640079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006574572,"about_ca_topic_score_gemma":0.006934017,"domain_scores_codex":[0.9963988,0.001418448,0.0001719563,0.0007552964,0.001040756,0.0002146782],"domain_scores_gemma":[0.9914996,0.005914397,0.0006737683,0.0009612192,0.0007867645,0.0001644098],"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":[0.0001413652,0.00006881473,0.00236884,0.000116994,0.0001647717,0.000173214,0.0002078101,0.7436272,0.002526554,0.1690004,0.001262863,0.08034115],"study_design_scores_gemma":[0.00001708337,0.00001811261,0.0002287221,0.00001474723,0.00002948283,0.00005702216,0.00001807916,0.9230788,0.0009319008,0.07445284,0.001125837,0.00002736339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001286699,0.0000537573,0.9981533,0.00007523828,0.000007815529,0.000006548442,0.00003081845,0.00008287695,0.0003029426],"genre_scores_gemma":[0.253517,0.0008313393,0.7406291,0.0002705003,0.0002908497,0.0002005508,0.0004383953,0.000162565,0.003659564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006574572,"threshold_uncertainty_score":0.02526551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01186822009126467,"score_gpt":0.2225215821991363,"score_spread":0.2106533621078716,"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."}}