{"id":"W3089017776","doi":"10.1145/3395027.3419598","title":"Automatic Generation of Electrical Plan Documents from Architectural Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Plan (archaeology); Floor plan; Computer science; Process (computing); Architectural plan; Code (set theory); Stack (abstract data type); Software engineering; Task (project management); Architectural engineering; Electrical equipment; Systems engineering; Architecture; Engineering drawing; Engineering; Electrical engineering; Programming language; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004245728,0.0009395074,0.0005907906,0.001454525,0.0003273242,0.0009487356,0.00100319,0.0006321878,0.004257683],"category_scores_gemma":[0.002766859,0.0004434416,0.0005952014,0.001128358,0.0003800775,0.001164246,0.0006803208,0.0007019727,0.002759985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003988128,"about_ca_system_score_gemma":0.0006449816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001509761,"about_ca_topic_score_gemma":0.002637321,"domain_scores_codex":[0.9995433,0.00007830554,0.00003898783,0.0001415917,0.0001702875,0.00002757768],"domain_scores_gemma":[0.9983271,0.0008202108,0.0001825494,0.0003537314,0.0002734694,0.00004288373],"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":[0.0004151094,0.0001082531,0.00180836,0.0004429939,0.00005165564,0.0005207034,0.0003303734,0.04784792,0.05877369,0.005276246,0.01540791,0.8690168],"study_design_scores_gemma":[0.00007165183,0.0001192732,0.001921881,0.0000603137,0.00004007285,0.0004189822,0.000274652,0.8265265,0.1317377,0.01047431,0.02829466,0.00006013101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0385243,0.0002033453,0.9087463,0.0001666353,0.00007371508,0.0001741972,0.002144466,0.04694089,0.003026248],"genre_scores_gemma":[0.2155642,0.000237997,0.7722183,0.00007519165,0.00003083141,0.0002045469,0.007219734,0.001353999,0.003095157],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004257683,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08344102083516063,"score_gpt":0.2910281524964023,"score_spread":0.2075871316612417,"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."}}