{"id":"W2995938608","doi":"10.3390/ijgi8120585","title":"Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition","year":2019,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Laser scanning; Computer science; Curvature; Principal component analysis; Outlier; Classifier (UML); Artificial intelligence; Computation; Pattern recognition (psychology); Structural engineering; Geology; Laser; Computer vision; Engineering; Algorithm; Mathematics; Geometry; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007512317,0.0001348689,0.0002581583,0.0001495933,0.00007306237,0.0001453825,0.0003216477,0.0001065224,0.0003420782],"category_scores_gemma":[0.0001580646,0.0001030253,0.0001354196,0.000134459,0.00004775019,0.004276465,0.00001267703,0.0002054952,0.0002393484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004616338,"about_ca_system_score_gemma":0.00006730329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002701791,"about_ca_topic_score_gemma":0.00002190677,"domain_scores_codex":[0.9978563,0.0001188401,0.00101753,0.00008045739,0.0007920422,0.0001348486],"domain_scores_gemma":[0.997982,0.0001271078,0.001245048,0.0001253682,0.0004646946,0.00005574102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02306512,0.0004121581,0.7086613,0.0107892,0.0032611,0.00007456795,0.03734145,0.03976183,0.02270999,0.01301391,0.11086,0.03004938],"study_design_scores_gemma":[0.01234501,0.004959135,0.8017913,0.009103128,0.0004405643,0.0009579366,0.02046286,0.1268121,0.009587849,0.004841467,0.007177247,0.001521453],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873705,0.0001144071,0.001044598,0.0003602866,0.002648917,0.0003032554,0.0001869741,0.00002734252,0.007943743],"genre_scores_gemma":[0.9985081,0.00007024266,0.0003465358,0.0001832344,0.0001517801,0.000001556927,0.0006887417,0.000002904715,0.00004692396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1036827,"threshold_uncertainty_score":0.420125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01770710652096602,"score_gpt":0.2297046080897454,"score_spread":0.2119975015687794,"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."}}