{"id":"W2511973019","doi":"10.5623/cig2016-202","title":"Boresight and Lever Arm Calibration of a Mobile Terrestrial LiDAR System","year":2016,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lever; Lidar; Calibration; Computer science; Remote sensing; Ranging; Inertial measurement unit; Computer vision; Artificial intelligence; Geology; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.00008993584,0.00005229088,0.00008587647,0.00001305355,0.00004706604,0.00001086407,0.00005522581,0.00003307284,0.0001655119],"category_scores_gemma":[0.00001618916,0.00003281069,0.00001837045,0.00005895314,0.0001061513,0.00006842094,0.00003693457,0.00001678319,0.00008748005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002798078,"about_ca_system_score_gemma":0.000005616945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001400795,"about_ca_topic_score_gemma":0.00001205327,"domain_scores_codex":[0.9994895,0.00002870298,0.0001534428,0.0001207325,0.000119584,0.00008802043],"domain_scores_gemma":[0.9996555,0.00005736432,0.00005836905,0.0001814123,0.000002186578,0.00004515047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001195224,0.0004185173,0.01654048,0.0001890611,0.00007205594,0.00001113177,0.003708466,0.0002398965,0.6751596,0.00732008,0.01778215,0.278439],"study_design_scores_gemma":[0.00844585,0.001580413,0.2701102,0.001540061,0.0003790148,0.0002658021,0.002416969,0.1023675,0.4209083,0.01783044,0.171982,0.002173458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795114,0.00001116724,0.01005166,0.0002448568,0.00004428717,0.000228867,0.000007585232,0.00004013877,0.009860057],"genre_scores_gemma":[0.9977716,0.000002917972,0.001832181,0.0000111896,0.0000310904,0.000005323299,0.000001279393,0.000005136235,0.0003392715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2762655,"threshold_uncertainty_score":0.1812239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007739570854499537,"score_gpt":0.2070755116314743,"score_spread":0.1993359407769748,"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."}}