{"id":"W2106817121","doi":"10.1109/icsmc.1989.71366","title":"Pre-marking methods for 3D object recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Perspective (graphical); Object (grammar); Computer science; Computer vision; Perspective distortion; Distortion (music); Feature extraction; Euclidean distance; Transformation (genetics); Cognitive neuroscience of visual object recognition; Process (computing); Class (philosophy); Pattern recognition (psychology); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001006626,0.00007344736,0.00008679889,0.00005278104,0.00005471041,0.0001060917,0.0002338825,0.00004175311,0.00008412711],"category_scores_gemma":[0.0003781579,0.00005926789,0.00004871265,0.0001230142,0.00001244646,0.0003388436,0.00002905438,0.0000528474,0.00001318089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002202166,"about_ca_system_score_gemma":0.00002134785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000328306,"about_ca_topic_score_gemma":0.000001509668,"domain_scores_codex":[0.9992982,0.0001061047,0.0001365746,0.000208459,0.00007802653,0.000172633],"domain_scores_gemma":[0.9994597,0.0001694283,0.000029712,0.0001934174,0.000109159,0.0000385258],"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":[0.000004367659,0.00003441457,0.00004408751,0.000009839684,0.000007767733,1.161091e-7,0.00007260426,1.053841e-7,0.01291104,0.1072602,0.0006124425,0.879043],"study_design_scores_gemma":[0.0002057305,0.0004808401,0.00009221449,0.00004543028,0.000009373381,0.000003865493,0.00001317264,0.01930746,0.741092,0.2056217,0.03286889,0.0002593021],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001522297,0.00002618272,0.8370346,0.00007910377,0.000171549,0.000207431,1.636922e-7,0.000263912,0.1620648],"genre_scores_gemma":[0.07041729,0.000005984784,0.9289328,0.0002349507,0.00001642976,0.00007078092,5.700944e-7,0.000004017099,0.0003172145],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8787838,"threshold_uncertainty_score":0.2416874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1090123097222307,"score_gpt":0.3825273944653593,"score_spread":0.2735150847431286,"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."}}