{"id":"W4243057245","doi":"10.5194/isprsarchives-xli-b4-295-2016","title":"GEOMETRIC CONTEXT AND ORIENTATION MAP COMBINATION FOR INDOOR CORRIDOR MODELING USING A SINGLE IMAGE","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University; Government of Ontario","keywords":"Computer science; Orientation (vector space); Context (archaeology); Artificial intelligence; Computer vision; Spatial contextual awareness; Pixel; Enhanced Data Rates for GSM Evolution; Point (geometry); Texture mapping; Line segment; Line (geometry); Geography; Mathematics; Geometry","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":[0.00032332,0.001023977,0.0006426476,0.001273716,0.0003098749,0.0009150717,0.0009297499,0.0006149914,0.001594002],"category_scores_gemma":[0.0008783684,0.0003802681,0.00104852,0.000686486,0.000487624,0.0008924821,0.001164544,0.0006121338,0.0005353173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003973786,"about_ca_system_score_gemma":0.0008113482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005888003,"about_ca_topic_score_gemma":0.009093781,"domain_scores_codex":[0.9995944,0.00005198111,0.00001446361,0.0001143604,0.0001377278,0.00008707315],"domain_scores_gemma":[0.9997348,0.00004673853,0.00004960868,0.00006194767,0.00007286687,0.00003413123],"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":[0.0006174434,0.0001868345,0.005556909,0.0002892967,0.0001277466,0.0005601347,0.0003080444,0.4516937,0.1040378,0.005898484,0.001736363,0.4289873],"study_design_scores_gemma":[0.00001143607,0.00009310497,0.00194375,0.00002157372,0.0000393673,0.0001759041,0.0001071072,0.9833771,0.01159789,0.001480061,0.001126579,0.00002616454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07090809,0.0002273161,0.9263484,0.00007343008,0.00002312355,0.0000775947,0.0001478656,0.0009294032,0.001264865],"genre_scores_gemma":[0.6449114,0.0003015496,0.3527642,0.00005985726,0.00004504852,0.0000974072,0.0005859038,0.0001343088,0.001100184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005888003,"threshold_uncertainty_score":0.01170748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02110445736299523,"score_gpt":0.2568165669675586,"score_spread":0.2357121096045633,"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."}}