{"id":"W2005119484","doi":"10.3390/rs6076500","title":"Moving Vehicle Information Extraction from Single-Pass WorldView-2 Imagery Based on ERGAS-SNS Analysis","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Science Fund for Distinguished Young Scholars; National Natural Science Foundation of China","keywords":"Computer science; Multispectral image; Panchromatic film; Computer vision; Artificial intelligence; Displacement (psychology); Correctness; Focus (optics); Remote sensing; Process (computing); Geography; Optics","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"],"consensus_categories":[],"category_scores_codex":[0.0003964278,0.0002915842,0.0003570319,0.0006871018,0.0001624128,0.0003066922,0.00009681708,0.000160024,0.00001546667],"category_scores_gemma":[0.0003876279,0.0003304833,0.0001994763,0.001028566,0.00003855418,0.0007535968,0.00001696283,0.0003436168,0.000213511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004325115,"about_ca_system_score_gemma":0.00001924187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000167478,"about_ca_topic_score_gemma":0.00005719112,"domain_scores_codex":[0.9982209,0.0001457082,0.0005509102,0.0002994526,0.0004181364,0.0003649197],"domain_scores_gemma":[0.9984115,0.000405849,0.0002019616,0.0007101243,0.0001537341,0.0001167723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000130048,0.000007845448,0.00002464751,0.00002336351,0.00006779363,0.000003492789,0.000100505,0.278547,0.1956356,0.000002391176,0.0001309706,0.5254434],"study_design_scores_gemma":[0.0002444191,0.00001542889,0.006067798,0.000129492,0.0002159026,0.000003274767,0.00004004759,0.9614626,0.02882138,0.00007480069,0.002605767,0.0003191307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.271641,0.00001961714,0.7203696,0.0002074025,0.0004409791,0.0001182044,0.000004354766,0.0006446323,0.006554235],"genre_scores_gemma":[0.913848,0.000007017046,0.08540198,0.0002487408,0.0002419559,2.773911e-8,0.0001662935,0.00005432691,0.00003161067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6829156,"threshold_uncertainty_score":0.9999147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110141182137648,"score_gpt":0.2135788806163178,"score_spread":0.2024774687949413,"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."}}