{"id":"W2563742413","doi":"","title":"LIDAR SYSTEM CALIBRATION: IMPACT ON PLANE SEGMENTATION AND PHOTOGRAMMETRIC DATA REGISTRATION","year":2010,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Lidar; Point cloud; Photogrammetry; Calibration; Remote sensing; Ranging; Laser scanning; Computer science; Segmentation; Computer vision; Artificial intelligence; Laser; Geology; Geodesy; Optics; Mathematics; Statistics","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":[0.001951402,0.0006541409,0.0004657022,0.001069102,0.000503025,0.001031829,0.0007832611,0.0007593619,0.0008351078],"category_scores_gemma":[0.01114955,0.0003557479,0.0004390359,0.001729974,0.0004849029,0.00108877,0.001061627,0.0005106447,0.0005158748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005425148,"about_ca_system_score_gemma":0.0007540416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004814623,"about_ca_topic_score_gemma":0.002614734,"domain_scores_codex":[0.9963342,0.0006230152,0.0002490062,0.0006987289,0.001832564,0.000262493],"domain_scores_gemma":[0.9939457,0.001815507,0.001066221,0.001397174,0.001697757,0.00007764676],"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":[0.001483227,0.0001755302,0.04968291,0.00065137,0.0002117738,0.0005732625,0.001135664,0.2970939,0.1851281,0.002889972,0.001987778,0.4589866],"study_design_scores_gemma":[0.00005971191,0.0005895077,0.09053737,0.0001156987,0.0001616374,0.001116366,0.0007189342,0.5665301,0.3283533,0.001654582,0.01002087,0.0001419651],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6933489,0.00173589,0.2975686,0.0002035608,0.0001711638,0.0001690881,0.000431144,0.003004502,0.003367184],"genre_scores_gemma":[0.9175687,0.0003697706,0.0800601,0.00007889496,0.0000158492,0.00005175499,0.0007591301,0.0003908013,0.0007049148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004814623,"threshold_uncertainty_score":0.01032007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01841750265200197,"score_gpt":0.2683187976049877,"score_spread":0.2499012949529857,"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."}}