{"id":"W4210271092","doi":"10.1088/1757-899x/1218/1/012009","title":"Slam and Beacon Data for Automation of Indoor Construction Progress Tracking","year":2022,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Beacon; Computer science; Real-time computing; Schedule; Process (computing); Automation; Data collection; Data quality; Systems engineering; Tracking system; Artificial intelligence; Engineering; Kalman filter","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":[0.001720515,0.0006852731,0.0006499577,0.002953031,0.0004323888,0.001292794,0.0009725824,0.0005919732,0.001203731],"category_scores_gemma":[0.002952283,0.0004394902,0.0005754473,0.003560192,0.0004548962,0.00146102,0.001307863,0.0006801906,0.0008200555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004313294,"about_ca_system_score_gemma":0.0009077849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004450648,"about_ca_topic_score_gemma":0.004714633,"domain_scores_codex":[0.9982033,0.0005665094,0.0001085031,0.0002727584,0.0007028914,0.000145953],"domain_scores_gemma":[0.9979994,0.0005430244,0.00026364,0.0004687547,0.0006676945,0.0000575281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003892647,0.0002218747,0.009768636,0.001095269,0.0001120883,0.0003484654,0.001004958,0.1166662,0.05146761,0.01118812,0.006287863,0.8014496],"study_design_scores_gemma":[0.00006721901,0.0003040283,0.01781408,0.0002515433,0.00009035503,0.0002503813,0.000942222,0.8837079,0.05734363,0.008515747,0.03060528,0.0001076303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06058494,0.0007478695,0.9306248,0.0001695117,0.0001107317,0.0001077012,0.000626429,0.003804335,0.003223652],"genre_scores_gemma":[0.6061784,0.0006376758,0.3900003,0.00004461318,0.00004516319,0.0001438192,0.001457747,0.0001911512,0.001301078],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004450648,"threshold_uncertainty_score":0.009099066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03342403851041759,"score_gpt":0.2282612356860734,"score_spread":0.1948371971756558,"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."}}