{"id":"W1630415446","doi":"10.14393/rbcv65n4-43857","title":"QUANTITATIVE EVALUATION AND QUALITY CONTROL OF COMMERCIALLY ADOPTED TRADITIONAL AND MODERN LIDAR SYSTEM CALIBRATION TECHNIQUES","year":2013,"lang":"en","type":"article","venue":"Revista Brasileira de Cartografia","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Lidar; Computer science; Calibration; STRIPS; Remote sensing; Data processing; Ranging; Process (computing); Global Positioning System; Quality (philosophy); Data quality; Real-time computing; Data mining; Artificial intelligence; Service (business); Geography; Mathematics; Database; Statistics; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01544185,0.0008815889,0.0005422464,0.004358902,0.0007638344,0.001587779,0.001346912,0.001224548,0.00130006],"category_scores_gemma":[0.02696444,0.0002830307,0.0005829917,0.003111946,0.001333073,0.001244113,0.001263291,0.0006081098,0.0003760829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001298274,"about_ca_system_score_gemma":0.0009251632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002726133,"about_ca_topic_score_gemma":0.002328293,"domain_scores_codex":[0.9785287,0.003290399,0.001177322,0.001554518,0.01507505,0.0003738382],"domain_scores_gemma":[0.9731549,0.006434043,0.00332129,0.003152383,0.01371405,0.0002233436],"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":[0.001078982,0.0006670484,0.06701443,0.002864083,0.0003669314,0.0003858674,0.002086182,0.04931488,0.220476,0.006362797,0.004831217,0.6445516],"study_design_scores_gemma":[0.0001928717,0.004947976,0.2449176,0.0007593135,0.0006931624,0.00149534,0.002607285,0.172867,0.5017735,0.004987292,0.06425411,0.0005045898],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6154335,0.009313199,0.3591759,0.0004080411,0.0004898901,0.001131721,0.001736354,0.001739492,0.01057201],"genre_scores_gemma":[0.8261244,0.001786934,0.1678266,0.0001375415,0.000105672,0.0005507792,0.001414694,0.0002823456,0.001771053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01544185,"threshold_uncertainty_score":0.08166528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04350448951191019,"score_gpt":0.2940065968529066,"score_spread":0.2505021073409964,"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."}}