{"id":"W2943188597","doi":"10.2113/eeg-2068","title":"Extraction and Comparison of Spatial Statistics For Geometric Parameters of Sedimentary Layers from Static and Mobile Terrestrial Laser Scanning Data","year":2019,"lang":"en","type":"article","venue":"Environmental and Engineering Geoscience","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Suncor Energy Incorporated","keywords":"Geology; Point cloud; Lithology; Stratigraphy; Sedimentary rock; Laser scanning; Spatial analysis; Remote sensing; Petrology; Laser; Computer science; Paleontology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005510873,0.0002842441,0.0001899515,0.003587364,0.0002346078,0.0006157678,0.000322355,0.000187661,0.0005489091],"category_scores_gemma":[0.002294879,0.0001122884,0.0002485041,0.002622969,0.0002082835,0.0002601879,0.0003026821,0.0001099477,0.0002538273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005915217,"about_ca_system_score_gemma":0.0007679043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06288312,"about_ca_topic_score_gemma":0.09786461,"domain_scores_codex":[0.999712,0.00002923013,0.00002327921,0.0000552579,0.0001464033,0.00003383961],"domain_scores_gemma":[0.9987139,0.0004007259,0.0001721444,0.0001112039,0.0005577163,0.00004437809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006412608,0.0002124533,0.4651307,0.0002331235,0.0001572505,0.0004084604,0.0006649952,0.0679895,0.1086917,0.0007421689,0.001252678,0.3538757],"study_design_scores_gemma":[0.00002311615,0.0001242077,0.6972571,0.00002308678,0.00007097301,0.0002153246,0.0009873272,0.2659128,0.03341134,0.0002682413,0.001659519,0.00004704937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965674,0.00006524909,0.03022729,0.0000226051,0.000004873567,0.00004389411,0.002388799,0.0006246179,0.0009487972],"genre_scores_gemma":[0.9562982,0.00004653988,0.03981161,0.000004646553,0.000004221272,0.00003138834,0.003424359,0.00003784589,0.0003411768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06288312,"threshold_uncertainty_score":0.1250342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01357929094747117,"score_gpt":0.2400655267907091,"score_spread":0.2264862358432379,"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."}}