{"id":"W2071758734","doi":"10.3182/20080706-5-kr-1001.00911","title":"An Adaptive Depth of Field Imaging System for Visual Servoing","year":2008,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Visual servoing; Computer vision; Artificial intelligence; Computer science; Field of view; Scheme (mathematics); Field (mathematics); Visualization; Depth of field; Image (mathematics); 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":[],"consensus_categories":[],"category_scores_codex":[0.00008442249,0.0001298023,0.0001710502,0.00007764105,0.0001453634,0.00003313773,0.0001731325,0.00005329735,0.00000224856],"category_scores_gemma":[0.00001653435,0.0001384327,0.00005257744,0.0001517129,0.00003496079,0.0003620917,0.00002021361,0.00008724276,0.000002088015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004246128,"about_ca_system_score_gemma":0.00001721664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002235331,"about_ca_topic_score_gemma":0.000001224856,"domain_scores_codex":[0.9993035,0.000001235388,0.0002135965,0.0001720306,0.00009863068,0.0002110094],"domain_scores_gemma":[0.9995925,0.00002118277,0.00006357285,0.00007197841,0.0001997232,0.00005101833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001866406,0.000326569,0.0448156,0.006420178,0.0001925651,0.00001045877,0.008858671,0.001095604,0.72585,0.0148321,0.02328167,0.1741299],"study_design_scores_gemma":[0.0002017103,0.0001275119,0.0005199113,0.0001984085,0.00002550882,0.00004703965,0.001086809,0.5388504,0.4575297,0.0003090781,0.0008406807,0.0002633114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4832471,0.0003826492,0.5087112,0.00005553529,0.00008040942,0.0004787522,0.00001227394,0.001814667,0.005217392],"genre_scores_gemma":[0.9219014,0.00001150919,0.07775477,0.00001967702,0.00009238304,0.0001442428,0.000003426238,0.00003785747,0.00003471871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5377548,"threshold_uncertainty_score":0.5645124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293017246050691,"score_gpt":0.2564894281110321,"score_spread":0.2435592556505252,"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."}}