{"id":"W2800336479","doi":"10.1016/j.isprsjprs.2018.03.025","title":"Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud","year":2018,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":102,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Point (geometry); Ceiling (cloud); Regularization (linguistics); Artificial intelligence; Computer vision; Engineering; Structural engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001088394,0.000279737,0.0004107177,0.0003040971,0.0005756023,0.0001644243,0.0001726824,0.0002054379,0.00003173474],"category_scores_gemma":[0.0003617636,0.000244654,0.0001873015,0.0009320586,0.0004184153,0.0001924393,0.0001137685,0.0006517026,0.00001162382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001886631,"about_ca_system_score_gemma":0.00007005886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001588691,"about_ca_topic_score_gemma":0.0000664335,"domain_scores_codex":[0.9978234,0.0001477011,0.0006910475,0.0003577551,0.0004740272,0.0005061142],"domain_scores_gemma":[0.9985952,0.0001760465,0.0004696411,0.0003157149,0.000132504,0.0003109199],"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":[0.0002503834,0.00008565362,0.0004665104,0.00005002679,0.0001051915,0.0001423329,0.00132728,0.09021123,0.355983,0.00001342692,0.00007359656,0.5512914],"study_design_scores_gemma":[0.0005079791,0.0001430655,0.00004837304,0.0006874412,0.00009441943,0.0006989407,0.0003308187,0.9313095,0.06299927,0.002446763,0.0004488994,0.0002845793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5083223,0.00005194841,0.4909214,0.0002163368,0.0002485346,0.00007809134,5.66539e-7,0.0000195654,0.0001412386],"genre_scores_gemma":[0.6757377,0.00001777079,0.3231249,0.0003773488,0.0006996297,5.885762e-9,6.097046e-7,0.00003250917,0.000009481456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8410982,"threshold_uncertainty_score":0.9976699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02097907868129951,"score_gpt":0.2832242882869505,"score_spread":0.262245209605651,"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."}}