{"id":"W4252721626","doi":"10.32920/ryerson.14661822.v1","title":"Automatic target matching","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Subpixel rendering; Computer vision; Epipolar geometry; Computer science; Matching (statistics); Similarity (geometry); Line (geometry); Template matching; Pixel; Image (mathematics); Pattern recognition (psychology); Mathematics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001173326,0.0002529224,0.0002906906,0.00006367647,0.00003295316,0.0004050456,0.0001604788,0.0002335359,0.001116247],"category_scores_gemma":[0.00001739269,0.0002565128,0.0001199767,0.0001252793,0.0000133115,0.00005955525,0.0001800592,0.0006472798,0.00006282738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005529925,"about_ca_system_score_gemma":0.00005270328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007639704,"about_ca_topic_score_gemma":0.000005053855,"domain_scores_codex":[0.9990973,0.0000185596,0.0002479043,0.0002386404,0.000152351,0.0002451797],"domain_scores_gemma":[0.9994411,0.0000406157,0.00002720191,0.0003725177,0.0000432687,0.00007524482],"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":[0.000002515936,0.0001413692,0.000837755,0.02215011,0.000917324,0.0004597576,0.006836733,0.311927,0.008045631,0.00009135271,0.008109439,0.6404811],"study_design_scores_gemma":[0.0001078366,0.000003635166,0.0002447515,0.0007934965,0.00005125905,0.00003935031,0.0004254522,0.9677008,0.02241609,0.00454029,0.002877023,0.0008000468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5316877,0.01831851,0.3723933,0.00005696567,0.00352362,0.0002269747,0.00001580575,0.00573392,0.06804319],"genre_scores_gemma":[0.8146456,0.0001936283,0.1844468,0.00009382958,0.0001367583,0.00002848101,0.0001078542,0.00007800599,0.0002689598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6557738,"threshold_uncertainty_score":0.9999887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163591541313263,"score_gpt":0.2347430769708124,"score_spread":0.2231071615576798,"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."}}