{"id":"W2791800592","doi":"10.1016/j.enggeo.2018.03.020","title":"Automated method for extracting and analysing the rock discontinuities from point clouds based on digital surface model of rock mass","year":2018,"lang":"en","type":"article","venue":"Engineering Geology","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Classification of discontinuities; Point cloud; Rock mass classification; Cluster analysis; Terrain; Geology; Robustness (evolution); Discontinuity (linguistics); Spurious relationship; Position (finance); Point (geometry); Computer science; Remote sensing; Algorithm; Artificial intelligence; Geometry; Mathematics; Geotechnical engineering; Geography; Cartography","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":[0.0002810257,0.0009544725,0.0008310909,0.003262719,0.0004395374,0.001107033,0.001651751,0.0007719197,0.002587337],"category_scores_gemma":[0.0007104967,0.0005610142,0.0007557354,0.001944942,0.0003076703,0.0008664717,0.0007424135,0.0006481842,0.001591947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003636664,"about_ca_system_score_gemma":0.00132604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006314355,"about_ca_topic_score_gemma":0.007728229,"domain_scores_codex":[0.9994016,0.0000291136,0.00003322085,0.0001517366,0.0003319569,0.00005242672],"domain_scores_gemma":[0.9994997,0.00006425588,0.00005280672,0.00009369401,0.0002653893,0.00002417857],"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":[0.0002082757,0.000201923,0.005471499,0.0003464689,0.0001065285,0.0003267929,0.0002178211,0.017777,0.1818908,0.002530467,0.006397529,0.7845249],"study_design_scores_gemma":[0.00009224084,0.0001513935,0.0237183,0.00005874412,0.0001638541,0.0009648637,0.0003344521,0.8217008,0.1301129,0.003458069,0.01914713,0.00009720811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01818932,0.0001380797,0.9758723,0.00003734258,0.000044171,0.0001480155,0.0006462648,0.004035828,0.0008887658],"genre_scores_gemma":[0.1580764,0.0003127514,0.8346471,0.00004196652,0.00003726775,0.0002796155,0.003506793,0.0002710595,0.002827078],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006314355,"threshold_uncertainty_score":0.01255524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01560919215822177,"score_gpt":0.2287847702365864,"score_spread":0.2131755780783646,"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."}}