{"id":"W2103544602","doi":"10.1109/crv.2005.81","title":"Topology Inference for a Vision-Based Sensor Network","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Network topology; Inference; Topology (electrical circuits); Wireless sensor network; Logical topology; Artificial intelligence; Computer vision; Computer network; Engineering; Electrical engineering","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":[0.0009390242,0.0007042808,0.0007357436,0.001925125,0.0007171715,0.0009058953,0.001578909,0.0008100384,0.001489885],"category_scores_gemma":[0.008264673,0.0007496161,0.0009123303,0.001117333,0.0008514917,0.002849881,0.0008786316,0.00130648,0.0004169062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001399929,"about_ca_system_score_gemma":0.0009619325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005991656,"about_ca_topic_score_gemma":0.00534803,"domain_scores_codex":[0.9992452,0.0001972256,0.00003876454,0.0001923855,0.0002801265,0.00004637999],"domain_scores_gemma":[0.9975613,0.001431645,0.0003860896,0.0002862405,0.0002473663,0.00008731356],"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":[0.00006970893,0.00002343291,0.001163547,0.0000716697,0.00004390291,0.00007926102,0.00006905883,0.9195284,0.002412208,0.01971937,0.0006119901,0.0562075],"study_design_scores_gemma":[0.000003994264,0.000009331804,0.0001571496,0.00000637424,0.000004451158,0.00002701635,0.000009585728,0.9831338,0.0005476334,0.01563212,0.0004626478,0.000005967885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006887108,0.0001414253,0.9921107,0.00009123282,0.00001398339,0.00001568022,0.00006998019,0.0003014972,0.0003684482],"genre_scores_gemma":[0.548777,0.000900559,0.4469747,0.0000856132,0.0001049024,0.0001640728,0.0009067349,0.0001582901,0.001928156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005991656,"threshold_uncertainty_score":0.0119136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009105875000312187,"score_gpt":0.2551123111981438,"score_spread":0.2460064361978316,"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."}}