{"id":"W1551700934","doi":"10.14393/rbcv56n1-43497","title":"CO-REGISTRATION OF PHOTOGRAMMETRIC AND LIDAR DATA: METHODOLOGY AND CASE STUDY","year":2009,"lang":"en","type":"article","venue":"Revista Brasileira de Cartografia","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Universidade Federal do Paraná","keywords":"Photogrammetry; Lidar; Computer science; Computer vision; Geodetic datum; Artificial intelligence; Remote sensing; Image registration; Orientation (vector space); Triangulation; Object (grammar); Geography; Image (mathematics); Mathematics; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.001002007,0.0001235757,0.0002352057,0.00007204665,0.0001234635,0.00006772693,0.0001387959,0.00005986896,0.00002033906],"category_scores_gemma":[0.0001719914,0.000116446,0.00002433263,0.00042553,0.0001964411,0.0001098289,0.000056182,0.0001073917,0.000004748684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000259258,"about_ca_system_score_gemma":0.00001233907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002865189,"about_ca_topic_score_gemma":0.0002900894,"domain_scores_codex":[0.9987589,0.0002465729,0.0002740546,0.0004021711,0.0001457251,0.0001726229],"domain_scores_gemma":[0.9989411,0.0001944859,0.0001321118,0.000603366,0.00001136973,0.0001175909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002432346,0.0003626557,0.7314374,0.00003685726,0.00003619581,0.000307972,0.001961905,0.00001250853,0.02982633,0.0002433054,0.001245146,0.2345054],"study_design_scores_gemma":[0.0006662206,0.0006118895,0.983968,0.00001780778,0.0001983317,0.003679825,0.002546265,0.001149151,0.0007659061,0.00008510707,0.006004867,0.0003066191],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947703,0.0002552693,0.003440857,0.00003460192,0.000009987131,0.0004117482,0.00001695912,0.00002981599,0.001030441],"genre_scores_gemma":[0.989781,0.00003259889,0.01002259,0.00007462407,0.00002278625,0.000001721733,0.00001942918,0.00000786909,0.00003738598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2525306,"threshold_uncertainty_score":0.4748529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07520709073640967,"score_gpt":0.3531390974163023,"score_spread":0.2779320066798927,"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."}}