{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001035736,0.0008934104,0.001012386,0.001063294,0.0006381046,0.000636499,0.001187416,0.0008200739,0.001492532],"category_scores_gemma":[0.003161728,0.0005089369,0.0008777145,0.001382416,0.001004017,0.002080894,0.001602708,0.001339442,0.0008388283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003984814,"about_ca_system_score_gemma":0.000811323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000870212,"about_ca_topic_score_gemma":0.0007556065,"domain_scores_codex":[0.9991653,0.000290913,0.00003072769,0.0001514303,0.0003159117,0.0000457843],"domain_scores_gemma":[0.9994386,0.0002370129,0.00007885116,0.0001117631,0.0001150442,0.00001863162],"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":[0.000146384,0.00006929855,0.000481687,0.0001948873,0.00008915548,0.0001738773,0.0002670459,0.4658644,0.01922127,0.07552335,0.004192033,0.4337767],"study_design_scores_gemma":[0.00002527272,0.0001150972,0.0001819405,0.00001626041,0.00003092698,0.0002241076,0.0000500844,0.9540903,0.008795586,0.02937026,0.007069829,0.00003039183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008617989,0.00005267638,0.998639,0.00003330544,0.000009609986,0.000009125586,0.00000498473,0.0001135712,0.0002758448],"genre_scores_gemma":[0.1166128,0.0003297465,0.8809029,0.00005915325,0.00008295733,0.0001960992,0.00009509182,0.0001561888,0.001565056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001492532,"threshold_uncertainty_score":0.005477548,"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."}}