{"id":"W2132072356","doi":"10.1109/have.2005.1545669","title":"Comparing ARTag and ARToolkit plus fiducial marker systems","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Fiducial marker; Computer science; Augmented reality; Artificial intelligence; Computer vision; Reliability (semiconductor); Feature extraction; Feature (linguistics); Simultaneous localization and mapping; Robot; Pose; Point (geometry); Object (grammar); Identification (biology); Mobile robot; 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":[0.00005561824,0.0000781347,0.0001115221,0.00003726932,0.00003525556,0.00006422642,0.00003008403,0.00004138035,0.00004297034],"category_scores_gemma":[0.000004513748,0.00007456666,0.00001412751,0.00004674664,0.000009656386,0.00006958719,0.000009050104,0.0000505607,0.00005035983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000388972,"about_ca_system_score_gemma":0.000004174694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002400462,"about_ca_topic_score_gemma":0.00004711085,"domain_scores_codex":[0.9995681,0.000007878465,0.0001350293,0.00008520533,0.0000740469,0.0001297401],"domain_scores_gemma":[0.9998229,0.00001659057,0.000008831858,0.00008682263,0.00001447142,0.00005034702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002594531,0.000003420571,0.001370317,0.00003101514,0.00001075573,0.000001182465,0.00005465029,0.991624,0.0002383701,0.003228188,0.002680654,0.0007548657],"study_design_scores_gemma":[0.0002087648,0.000004418564,0.002126961,0.00001385355,0.000005771291,0.000004740047,0.00003141168,0.9847943,0.000179102,0.000007672495,0.01252058,0.0001024927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6719038,0.0006867117,0.2453697,0.0001246832,0.0006677804,0.0002250096,0.000002151869,0.0004998009,0.08052034],"genre_scores_gemma":[0.9981319,0.00002782598,0.0008145011,0.00003363816,0.0002130859,0.000002642311,0.00000617917,0.00001600138,0.0007541875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3262281,"threshold_uncertainty_score":0.304074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01451620619801274,"score_gpt":0.1933365745280407,"score_spread":0.1788203683300279,"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."}}