{"id":"W1885234456","doi":"10.1016/j.proeng.2015.10.085","title":"Enhanced Localization for Indoor Construction","year":2015,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Environmental science; Architectural engineering; 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":[0.0002602065,0.0005610647,0.0004593083,0.0004314925,0.0001409671,0.0004988919,0.0005711025,0.0005877094,0.001914572],"category_scores_gemma":[0.0007186075,0.0002286736,0.0004398643,0.0005026883,0.0002850417,0.0007874494,0.0008027423,0.000335806,0.0009708067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003615478,"about_ca_system_score_gemma":0.0003646057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00206835,"about_ca_topic_score_gemma":0.002659276,"domain_scores_codex":[0.9996601,0.00008872643,0.000008233662,0.00006906701,0.0001401208,0.00003370496],"domain_scores_gemma":[0.9997742,0.0000737946,0.00003641091,0.0000503842,0.00005772689,0.000007515329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002271314,0.00008941787,0.004302018,0.0005353986,0.00007459723,0.000409428,0.000283983,0.4557751,0.107298,0.01480525,0.003119665,0.4130799],"study_design_scores_gemma":[0.000026273,0.0003240585,0.005290603,0.00005580574,0.00006827772,0.0006483199,0.0001135965,0.9371453,0.02931423,0.005205758,0.02174793,0.00005981643],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01828668,0.0004582417,0.977511,0.00009079213,0.00004362732,0.00001490585,0.00005354744,0.0009106543,0.002630509],"genre_scores_gemma":[0.8117016,0.001124164,0.1780573,0.0000744808,0.00004180146,0.00006010078,0.0002613773,0.00009468597,0.008584419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00206835,"threshold_uncertainty_score":0.006404817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170296612265542,"score_gpt":0.2021966352355975,"score_spread":0.190493669112942,"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."}}