{"id":"W6926873099","doi":"10.25545/6dgf1v","title":"Camera-LiDAR Datasets","year":2024,"lang":"en","type":"dataset","venue":"UNB Dataverse","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Lidar; Mobile mapping; Data collection; Matching (statistics); Tree (set theory); Aerial survey; Laser scanning; Data acquisition","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006863899,0.001092005,0.0008557144,0.001007838,0.000170208,0.0004876419,0.002989595,0.0006662884,0.05052999],"category_scores_gemma":[0.0004758812,0.001069746,0.0002363451,0.0009435222,0.0003444806,0.0006655015,0.002841227,0.001918159,0.9711413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004330815,"about_ca_system_score_gemma":0.0005396554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002224304,"about_ca_topic_score_gemma":0.001990846,"domain_scores_codex":[0.9948933,0.0002110421,0.0007356662,0.001905798,0.001221491,0.001032759],"domain_scores_gemma":[0.9921215,0.0001376467,0.0003365516,0.006856041,0.00006613672,0.0004821225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005353741,0.000156704,6.215833e-7,0.0004874679,0.0003674207,0.00348513,0.00001259291,0.000002308282,0.00002824188,0.00002493791,0.9951891,0.0001918848],"study_design_scores_gemma":[0.0004799113,0.00008116761,0.000001242065,0.0003624668,0.001199777,0.0001667558,0.00006107974,0.00001448101,0.00002569469,0.00005004302,0.9964183,0.001139085],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004091538,0.0001007077,0.000001184584,0.00002223424,0.002813379,0.0005902699,0.9956271,0.0004592115,0.0003817947],"genre_scores_gemma":[8.003682e-7,0.0003319925,0.0001775719,0.0004654961,0.001109239,0.00005257985,0.997004,0.0002949796,0.0005632835],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9206113,"threshold_uncertainty_score":0.9991753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01906722288432096,"score_gpt":0.2888665046634527,"score_spread":0.2697992817791318,"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."}}