{"id":"W2967919155","doi":"10.22161/ijaers.68.11","title":"Compatibility Evaluation of Point Clouds Acquired with Terrestrial and Mobile LiDAR Scanners","year":2019,"lang":"en","type":"article","venue":"International Journal of Advanced Engineering Research and Science","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Géomatique du Québec","funders":"Universidade Estadual de Campinas; Université Laval","keywords":"Remote sensing; Lidar; Point cloud; Environmental science; Compatibility (geochemistry); Computer science; Geography; Geology; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002411367,0.00005013675,0.00009354867,0.0001329585,0.00004772348,0.00004698107,0.0002470017,0.00001429948,0.00002427663],"category_scores_gemma":[0.0002497995,0.00003883618,0.0000134175,0.0002764165,0.0004496649,0.0003665142,0.00008874141,0.0001022852,0.000002158573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001662406,"about_ca_system_score_gemma":0.00009149907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004381474,"about_ca_topic_score_gemma":0.000004579258,"domain_scores_codex":[0.9980618,0.00002514373,0.0001995752,0.0001506725,0.00142613,0.000136724],"domain_scores_gemma":[0.9993573,0.0001032553,0.00009348175,0.00010861,0.0002350552,0.000102251],"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":[0.0002672735,0.00008680856,0.01998048,0.000007991853,0.00002126698,0.000003265699,0.0009908308,0.2986706,0.5917376,0.00009419376,0.00001966241,0.08811998],"study_design_scores_gemma":[0.003900656,0.002099735,0.5264273,0.0003591307,0.00001806201,0.000254439,0.001362802,0.3817195,0.07983419,0.001981221,0.001745891,0.0002970735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985529,0.00004716186,0.0005843202,0.0001113805,0.0001196682,0.000154254,0.000001285013,0.000003272728,0.0004257952],"genre_scores_gemma":[0.9950067,0.00002347394,0.004923713,0.000003136076,0.00002649819,0.000001203106,4.129256e-7,0.000003224652,0.00001166207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5119034,"threshold_uncertainty_score":0.1656809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102111039860222,"score_gpt":0.3328620120956425,"score_spread":0.3118409016970403,"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."}}