{"id":"W7044120335","doi":"","title":"Valutazione delle prestazioni di sistemi di acquisizione tipo 3D active vision: alcuni risultati","year":2003,"lang":"it","type":"article","venue":"NPARC","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Active vision; Machine vision; Construct (python library); High resolution","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032332,0.001036171,0.001044309,0.002530002,0.0004685855,0.003643398,0.001164788,0.001715213,0.003316303],"category_scores_gemma":[0.01035986,0.0006656779,0.0004613524,0.001531268,0.001111475,0.004363722,0.001406034,0.001518217,0.001986057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007210994,"about_ca_system_score_gemma":0.0006553961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006124063,"about_ca_topic_score_gemma":0.0006868241,"domain_scores_codex":[0.9974873,0.0004949178,0.000093795,0.000409201,0.001411064,0.0001036279],"domain_scores_gemma":[0.9947637,0.00302637,0.0003440398,0.0005692322,0.00120255,0.00009407172],"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":[0.0002094723,0.00007432649,0.004408815,0.001162618,0.00007946367,0.0002162169,0.0003799371,0.01942294,0.1289191,0.02868478,0.00283087,0.8136115],"study_design_scores_gemma":[0.00006018132,0.0007271045,0.01224399,0.000676141,0.0003357589,0.002600587,0.0004144227,0.4083463,0.4095127,0.06423634,0.1005896,0.0002568734],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03621921,0.03429998,0.9174325,0.0009795272,0.0002356025,0.00007948897,0.0001367516,0.001131512,0.009485563],"genre_scores_gemma":[0.4197944,0.02085461,0.553048,0.00032795,0.0003583632,0.0001495281,0.00030263,0.0003886014,0.004775978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003643398,"threshold_uncertainty_score":0.01709896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03245937128017858,"score_gpt":0.282795567920565,"score_spread":0.2503361966403864,"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."}}