{"id":"W4387217811","doi":"10.36487/acg_repo/2325_02","title":"High-resolution ground-deformation and support monitoring using a portable handheld LiDAR approach","year":2023,"lang":"en","type":"article","venue":"","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Glencore (Canada); RTDS Technologies (Canada)","funders":"","keywords":"Lidar; Point cloud; Remote sensing; Computer science; Deformation monitoring; Ranging; Computer vision; Environmental science; Deformation (meteorology); Geology; Geography; Meteorology; Telecommunications","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":[0.000128567,0.0003628524,0.0002555156,0.0006547752,0.0001532092,0.0004293456,0.0006943957,0.0004383977,0.001683643],"category_scores_gemma":[0.0002808272,0.0001365617,0.0002379686,0.0005506426,0.00009765066,0.000492636,0.0003899993,0.0002170685,0.0005011222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002320972,"about_ca_system_score_gemma":0.0002283533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001564749,"about_ca_topic_score_gemma":0.002821075,"domain_scores_codex":[0.9997726,0.00002444432,0.000005679804,0.0000493982,0.0001259277,0.00002185651],"domain_scores_gemma":[0.9998813,0.00002239392,0.00001959158,0.00003056383,0.00003723349,0.000008968515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003366196,0.000249845,0.01113338,0.0002789834,0.00006863081,0.0007194163,0.0002053353,0.03106632,0.5943469,0.000713105,0.001703355,0.359178],"study_design_scores_gemma":[0.0001482518,0.001397759,0.06366107,0.0001079362,0.0001363178,0.001704016,0.0005629935,0.597105,0.3192739,0.00242254,0.01329965,0.0001805899],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5938424,0.0005995936,0.3936478,0.0001518591,0.00008764417,0.0002568295,0.0009120243,0.002609552,0.007892379],"genre_scores_gemma":[0.8581585,0.0001781739,0.1390859,0.00005879408,0.00002348272,0.00007272587,0.0003604794,0.00004375393,0.002018094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001683643,"threshold_uncertainty_score":0.005632401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04911950075975665,"score_gpt":0.234851605162009,"score_spread":0.1857321044022523,"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."}}