{"id":"W1493185040","doi":"","title":"An optimal local map registration technique for wireless sensor network localization problems","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Wireless sensor network; Affine transformation; Pairwise comparison; Rotation (mathematics); Global Map; Set (abstract data type); Local search (optimization); Artificial intelligence; Algorithm; Computer vision; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001831662,0.0001997691,0.0001452302,0.0002325231,0.0002371872,0.0001062228,0.0002856042,0.0002411444,0.0001471129],"category_scores_gemma":[0.00002899205,0.0002010648,0.00005302925,0.0001738507,0.00008996944,0.001228298,0.00002286416,0.0001603091,0.00007073148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001566688,"about_ca_system_score_gemma":0.00005460222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000107628,"about_ca_topic_score_gemma":0.000008115786,"domain_scores_codex":[0.9987144,0.00001938362,0.0005224436,0.0001508495,0.0003700699,0.0002227931],"domain_scores_gemma":[0.999006,0.00002543876,0.0001504397,0.0002138322,0.0005471523,0.00005711734],"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":[0.0001165411,0.00003647001,0.0001364508,0.00007690675,0.00001939022,0.000001428347,0.0005194166,0.9099128,0.002115163,0.06877643,0.006469716,0.01181932],"study_design_scores_gemma":[0.0004491067,0.0001755505,0.00009026196,0.00009539576,0.000004703925,0.00002042235,0.0003129533,0.9548683,0.02296946,0.001225066,0.01952624,0.0002625473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003964496,0.000006695574,0.9875045,0.0002372869,0.0006770605,0.0007154586,0.000050034,0.0007761256,0.00606833],"genre_scores_gemma":[0.9922641,0.0001327199,0.005509051,0.0001861074,0.0001489336,0.00030602,0.001336109,0.00002104386,0.00009594793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9882995,"threshold_uncertainty_score":0.8199186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02030934711322222,"score_gpt":0.2430571020136282,"score_spread":0.222747754900406,"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."}}