{"id":"W2137151237","doi":"10.3390/rs71013029","title":"A 4D Filtering and Calibration Technique for Small-Scale Point Cloud Change Detection with a Terrestrial Laser Scanner","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Pacific Railway (Canada); BGC Engineering (Canada); Queen's University","funders":"Université de Lausanne; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Point cloud; Smoothing; Computer science; Laser scanning; Remote sensing; Deformation monitoring; Scale (ratio); Calibration; Scale space; Change detection; Redundancy (engineering); Geology; Algorithm; Deformation (meteorology); Computer vision; Laser; Mathematics; Image processing; Geography; Optics; Physics; Image (mathematics)","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":[0.0006507967,0.0005875276,0.0005489567,0.001878517,0.000564658,0.0006135654,0.0008191519,0.0007916586,0.002507518],"category_scores_gemma":[0.001654559,0.0005211103,0.0007995516,0.00187121,0.0003810198,0.0007481324,0.0007594335,0.0008042407,0.001229143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004288036,"about_ca_system_score_gemma":0.000693782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002400654,"about_ca_topic_score_gemma":0.003789194,"domain_scores_codex":[0.999246,0.00007738056,0.00003482238,0.0001395535,0.000452136,0.00005018872],"domain_scores_gemma":[0.9993807,0.0001520136,0.00008304306,0.000150407,0.0002120363,0.00002177428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001380876,0.000100823,0.003924546,0.0001678862,0.00007478956,0.0001803401,0.0002298949,0.03539586,0.1832622,0.004239601,0.002583084,0.7697029],"study_design_scores_gemma":[0.00003180582,0.0002070257,0.01588318,0.00003862115,0.00005752768,0.00136072,0.0001335873,0.8207036,0.1253682,0.004214656,0.03185856,0.0001425223],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01027999,0.00006429579,0.9880315,0.00004836686,0.00003698918,0.00004846525,0.00009134066,0.0006706225,0.0007282664],"genre_scores_gemma":[0.0594684,0.0000980667,0.9389201,0.00004010645,0.00002305005,0.0000977772,0.000278527,0.0001064399,0.0009675813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002507518,"threshold_uncertainty_score":0.00838846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03207666344489297,"score_gpt":0.2375930877290358,"score_spread":0.2055164242841429,"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."}}